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We present a detailed and actionable guide on creating a replication package, relying on our expertise as data editors of leading journals.

DCASis a standard for sharing research code and data, endorsed byleading journalsin social sciences. It is maintained by theSocial Science Data Editors.

The typical README in social science journals serves the purpose of guiding a reader through the available material and a route to replicating the results in the research paper, including the description of the origins of data and/or description of programs. As such, a good README file should first provide a brief overview of the available material and a brief guide as to how to proceed from beginning to end, before then diving into the specifics. These template files structure such a README in a way that is compliant with the typical data and code workflow in the social sciences., The README is under a CC-BY-NC license. Usage by commercial entities is allowed, reselling it is not.

Files Use forward slash in path names Write save "data/worker.dta", not save "data\worker.dta". The former works on all three major platforms, the latter only on Windows. Write out file extensions Write save "data/worker.dta" and do "regression.do", not save "data/worker" or do "regression". Even though some extensions are appended by Stata by default, it is better to be explicit to help future readers of your code. Put file paths in quotes Write save "data/worker.dta" and do "regression.do", not save data/worker.dta or do regression. Both are correct, but the first is more readable, as most editors readily highlight strings as separate from programming statements. Use relative path whenever possible Write save "../data/worker.dta", not save "/Users/koren/Tresorit/research/data/worker.dta. Nobody else will have the same absolute path as you have on your system. Adopt a convention of where you are running scripts from and make paths relative to that location. Naming Do not abbreviate commands Use generate ln_wage = ln(wage) and summarize ln_wage, detail, not g ln_wage = ln(wage) or su ln_wage, d. Both will work, because Stata allows you abbreviation, but the former is more readable. Do not abbreviate variable names Use summarize ln_wage, detail, not summarize ln_w, detail. Both will work, because Stata allows you abbreviation, but the latter is very error prone. In fact, you can turn off variable name abbreviation with set varabbrev off, permanent. Use verbose names to the extent possible Use egen mean_male_wage = mean(wage) if gender == "male" , not egen w1 = mean(wage) if gender == "male". Your variables should be self documenting. Reserve variable labeling to even more verbose explanations, including units: label variable mean_male_wage "Average wage of male workers (2011 HUF)". Separate name components with underscore Use egen mean_male_wage = mean(wage) if gender == "male" , not egen meanmalewage = mean(wage) if gender == "male" or egen meanMaleWage = mean(wage) if gender == "male". The former is more readable. Transformations like mean, log should be part of the variable name. Do not put units and time in the variable name Use revenue , not revenue_USD or revenue_2017. Record this information in variable labels, though. You will change your code and your data and you don't want this detail to ruin your entire code. It is ok to use short macro names in short code If you have a foreach loop with a few lines of code, it is fine to use a one-character variable name for indexing: foreach X of variable wage mean_male_wage {. But if you have longer code and X would pop up multiple times, give it a more verbose name. It is ok to use obvious abbreviation in variable names If you are hard pressed against the 32-character limit of variable name length, use abbreviation that will be obvious to everyone seeing the code. Use generate num_customer, not generate number_of_customers_of_the_firm or generate n_cust. White space Include a space around all binary operators Use generate ln_wage = ln(wage) and count if gender == "male", not generate ln_wage=ln(wage) or count if gender=="male". The former is more readable. Include a space after commas in function calls Use assert inlist(gender, "male", "female") not assert inlist(gender,"male","female"). The former is more readable. Indent code that belongs together foreach X of variable wage mean_male_wage { summarize `X', detail scalar `X'_median = r(p50) } not foreach X of variable wage mean_male_wage { summarize `X', detail scalar `X'_median = r(p50) } Each .do file should be shorter than 120 lines Longer scripts are much more difficult to read and understand by others. If your script is longer, break it up into smaller components by creating several .do files and calling them.

A közgazdaságtanban általában a cégekre olyan szereplőkként gondolunk, amelyek csak anonim piaci mechanizmuson keresztül érintkeznek egymással. Ezzel szemben a valóságban a cégek életében fontos szerepet játszanak a gazdaság más, konkrét szereplőivel meglévő kapcsolataik. Tanulmányunkban a vállalati hálózatok közgazdaságtanának elméleti és empirikus irodalmát összegezzük. Az áttekintett művek szerint a vállalati kapcsolatok hálózati jellege pozitív és negatív hatást egyaránt gyakorol a gazdaság egészére. A mikroszintű adatok újszerű vizsgálataiból pedig gazdag kép bontakozik ki a vállalatok viselkedéséről, például a technológiai fejlődés, a hitelezés, az ellátási láncok vagy akár a korrupció szempontjából. Journal of Economic Literature (JEL) kód: L14, D20, F10, G10, G20., Economic theory mainly models firms as interacting through anonymous market mechanisms. In practice, however, the connections of these firms to other specific actors play an important role. The paper surveys the theoretical and empirical literature on such economic networks, which finds that the networked nature of firm connections has both positive and negative effects on the economy as a whole. Novel analyses of micro-level datasets paint a rich picture of firm behaviour in a number of domains, including technological progress, borrowing and lending, supply chains, and corruption.

A minimálbér-emelés a munkapiacon közvetlenül hat a keresletre és a kínálatra. Közvetett hatásai azonban túlmutatnak a munkapiacon, ezért azokat egy makromodell keretei között elemezzük. A makromodellben háromféle munkafajta és tíz ágazat van; az egyes ágazatok az árképzésükben és az adó- és járulékelkerülésük szerkezetében különböznek. A minimálbér-emelés munkapiaci feszültséget generál: csökkenti a foglalkoztatást a szakképzetlenek körében. Mivel az árszint az átlagbérnél gyorsabban nő, és az aggregált foglalkoztatás is csökken, így csökken a reálfogyasztás. A vállalatok profitja és beruházása csökken, ugyanakkor a vállalati profit csökkenése már csekély mértékű adóelkerülés-növeléssel is kiegyensúlyozható. A minimálbér-emelés hatására nőnek ugyan az adóbevételek, viszont a kiadások nagyobb mértékben nőnek, így általában romlik az egyenleg. Aki tehát a minimálbér emelését követeli, annak a felelős döntés során számolnia kell ezekkel a következményekkel.* Journal of Economic Literature (JEL) kód: E61, H26, H62, J30., Raising the minimum wage on the labour market has direct effects on supply and demand. But its indirect effects extend beyond the labour market. They are analysed here with a macro model that distinguishes three types of work and ten industries, whose firms differ in their price structures and the degrees to which tax and social-insurance payments are avoided. Raising the minimum wage generates tension on the labour market and reduces employment of the unskilled. Since the price level rises faster than average pay and aggregate employment falls, so does real consumption. The firms’ profits and investment decline, but the former can be offset even by a small increase in tax avoidance. Although the rise in the minimum wage boosts tax revenues, budgetary expenditures rise more and the balance deteriorates. Advocates of a higher minimum wage need to consider these consequences if they are to reach a responsible decision.

We estimate the effect of imported machines on the wages of machine operators utilizing Hungarian linked employer-employee data. We infer exposure to imported machines from detailed trade statistics of the firm and the occupation description of the worker. We find that workers exposed to imported machines earn about 8 percent higher wages than other machine operators at the same firm. When we proxy for unobserved worker characteristics, we find a significant 3 percent wage premium, suggesting that the relationship is causal. The return to schooling is also higher on imported machines. We build a simple matching model consistent with these findings. Our findings suggest that machine imports can be an important channel through which skill-biased technical change reaches less developed and emerging economies.

We estimate the effect of imported machines on the wages of machine operators utilizing Hungarian linked employer-employee data. We infer exposure to imported machines from detailed trade statistics of the firm and the occupation description of the worker. We find that workers exposed to imported machines earn about 8 percent higher wages than other machine operators at the same firm. When we proxy for unobserved worker characteristics, we find a significant 3 percent wage premium, suggesting that the relationship is causal. The return to schooling is also higher on imported machines. We build a simple matching model consistent with these findings. Our findings suggest that machine imports can be an important channel through which skill-biased technical change reaches less developed and emerging economies.

The theory of geographical markets is based on the notion that economic activity is not evenly spread and regional inequalities have an impact on the decisions of economic agents. Retail gasoline markets are almost perfect examples of geographical markets. The gasoline sold by the stations is a fairly homogeneous good and hence, stations compete for customers locally only. In this study we analyze how market structure, geographical attributes and in particular, demand will influence prices. Our work is a natural follow-up to the study of Csorba et al. (2009) who showed that Hungarian retail gasoline prices are affected by conditions of market competition. Compared to the related literature there are two novelties in this study. First, the dataset allows us combining station specific and local attributes to explain prices. With some chains applying different strategies compared to smaller firms, this is an important complementary feature compared to the studies relying on purely region/local market specific attributes. Second, we extend the estimation of the demand side and partly use it as instruments. Local GDP and population density are two common proxies for demand. We extend the analysis from households to contain firms' and transit traffic demand. To estimate the elasticity of demand we use quantitative (population, number of firms and tourists) and income-effect (per capita income, ratio of big firms and foreign tourists) variables. This methodology enables us to instrument station density, used in classical price estimation models, (partly) with demand factors. We find that demand influences prices mainly through the number of stations. There is some local price variation across chains, and the composition of station has a small impact as well. It is an interesting result, which, along with highways and country borders, has an effect on price determination.

We use Hungarian Customs data on product-level imports of manufacturing firms to document that the import price of a particular product varies substantially across buying firms. We relate the level of import prices to firm characteristics such as size, foreign ownership, and market power. We develop a theory of “pricing to firm” (PTF), where markups depend on the technology and competitive environment of the buyer. The predictions of the model are confirmed by the data: import prices are higher for firms with greater market power, and for more essential intermediate inputs (with a high share in material costs). We take account of the endogeneity of the buyer’s market power with respect to higher import prices and unobserved cost heterogeneity within product categories. The magnitude of PTF is big: the standard deviation of price predicted by PTF is 21.5%.

What is the effect of imports on productivity? To answer this question, we estimate a structural model of producers using product-level import data for a panel of Hungarian manufacturing firms from 1992 to 2001. In our model with heterogenous firms, producers choose to import or purchase domestically varieties of intermediate inputs. Imports affect firm productivity through expanding variety as well as improved input quality. The model leads to a production function where the total factor productivity of a firm depends on the share of inputs imported. To estimate this import-augmented production function, we extend the Olley and Pakes (1996) procedure for a setting with an additional state variable, the number of input varieties imported. Our results suggest that the role of imports is both statistically and economically significant. Imports are responsible for 30% of the growth in aggregate total factor productivity in Hungary during the 1990s. About 50% of this effect is through imports advancing firm level productivity, while the remaining 50% comes from the reallocation of capital and labour to importers.

Why is GDP so much more volatile in poor countries than in rich ones? To answer this question, we propose a theory of technological diversification. Production makes use of different input varieties, which are subject to imperfectly correlated shocks. As in endogenous growth models, technological progress increases the number of varieties, raising average productivity. The new insight is that an expansion in the number of varieties also lowers the volatility of output. This is because additional varieties provide diversification benefits against variety-specific shocks. In the model, technological complexity evolves endogenously in response to profit incentives. Complexity (and hence output stability) is positively related with the development of the country, the comparative advantage of the sector, and the sector's skill and technology intensity. Using sector-level data for a broad sample of countries, we provide extensive empirical evidence confirming the cross-country and cross-sectoral predictions of the model.

A minimálbér-emelés a munkapiacon közvetlenül hat a keresletre és a kínálatra. Közvetett hatásai azonban túlmutatnak a munkapiacon, ezért azokat egy makromodell keretei között elemezzük. A makromodellben háromféle munkafajta és tíz ágazat van; az egyes ágazatok az árképzésükben és az adó- és járulékelkerülésük szerkezetében különböznek. A minimálbér-emelés munkapiaci feszültséget generál: csökkenti a foglalkoztatást a szakképzetlenek körében. Mivel az árszint az átlagbérnél gyorsabban nő, és az aggregált foglalkoztatás is csökken, így csökken a reálfogyasztás. A vállalatok profitja és beruházása csökken, ugyanakkor a vállalati profit csökkenése már csekély mértékű adóelkerülés-növeléssel is kiegyensúlyozható. A minimálbér-emelés hatására nőnek ugyan az adóbevételek, viszont a kiadások nagyobb mértékben nőnek, így általában romlik az egyenleg. Aki tehát a minimálbér emelését követeli, annak a felelős döntés során számolnia kell ezekkel a következményekkel. _____ Raising the minimum wage on the labour market has direct effects on supply and demand. But its indirect effects extend beyond the labour market. They are analysed here with a macro model that distinguishes three types of work and ten industries, whose firms differ in their price structures and the degrees to which tax and social-insurance payments are avoided. Raising the minimum wage generates tension on the labour market and reduces employment of the unskilled. Since the price level rises faster than average pay and aggregate employment falls, so does real consumption. The firms’ profits and investment decline, but the former can be offset even by a small increase in tax avoidance. Although the rise in the minimum wage boosts tax revenues, budgetary expenditures rise more and the balance deteriorates. Advocates of a higher minimum wage need to consider these consequences if they are to reach a responsible decision.

The Paper investigates how foreign-owned and domestic firms in Hungary set their export prices. Using a unique dataset with firm and product-level data on trade flows, we find that foreign firms charge substantially lower export prices than domestic firms. This finding is robust after controlling for relevant firm characteristics, indicating that the pricing behaviour of foreign firms is different. One explanation is that multinational firms engage in transfer pricing to minimize tax burden. We provide evidence that tax incentives explain a large portion of the price gap; the export prices of tax-paying foreign firms are 15 to 35% lower than arm’s length prices.

The present Paper investigates the effects of incorporating illiquidity in a standard dynamic portfolio choice problem. Lack of liquidity means that an asset cannot be immediately traded at any point in time. We find the portfolio share of financial wealth invested in illiquid assets given the liquidity premium. Benchmark calibrations imply a portfolio share of 2-6% in cash. These numbers are in line with survey data and also with portfolio recommendations by practitioners. We also find that long horizon investors invest more in illiquid assets. Overall, our results suggest that differences between asset classes unrelated to standard price risk may influence portfolio shares.

This paper estimates the labor demand response of Hungarian exporting firms to real exchange rate movements. The use of firm level export–import data enables the separation of two channels through which the exchange rate affects labor demand. First, a real depreciation raises the forint-equivalent price of foreign competitors, thereby boosting demand for the firm’s export and, hence, the firm’s demand for labor. Second, by raising the cost of imported inputs, a depreciation has an adverse effect on employment through the cost channel. A higher marginal cost induces a decrease in production and thus shrinks labor demand. Since firms with higher export share tend to import more, this latter negative effect might offset the former positive one. The cost effect may be dampened if labor and imported inputs are substitutes. The paper shows that the relative importance of the demand and cost effects is industry specific. The short-run exchange rate and employment elasticity stemming from the demand effect is around 0.04. This channel is most pronounced in the case of the Food and tobacco industry. Machinery, on the other hand, exhibits a cost effect of roughly -0.04. Surprisingly, there is no evidence that export share affects exchange rate exposure.