rafmacalaba/datause-encoder-data huggingface dataset:
representative household assumption and its associated theoretical shortcomings are dropped almost entirely. However, these accomplishments entail higher data requirements and computational costs, and may require other dimensions of the model to be scaled back (e.g., the authors are only able to consider three sectors and four commodities).

Cogneau and Robilliard analyze a variety of sector-level shocks to Madagascar’s economy including, among others, shocks to the world price of cash crops. Without exception, they find that the impact of poverty and inequality is small. They give three reasons for this. Firstly, they argue that many of the households are diversified in their sources of income and so the impact of sector-specific shocks are muted. Secondly, to the extent that households are not initially diversified, they may respond to the external shock by changing their allocation of labor, in an effort to equate factor returns across sectors, thereby spreading the impact beyond the directly affected sector. Finally, they make the point that the second-round benefits of any favorable development tend to accrue to those individuals who control the factors of production in the economy. Thus a disproportionate share of the gains go to the wealthy, preserving the initial degree of inequality. This stability of the overall income distribution in the face of policy interventions has long been observed by those working in this field (e.g., Dervis, de Melo and Robinson, 1982).<sup>8</sup>

# **V. Micro-Macro Syntheses**

While the approach of Cogneau and Robilliard is innovative and ambitious, it remains beyond the reach of most CGE modelers seeking to obtain some basic insights into the links between trade and poverty. This is particularly true in larger countries, where much greater sector detail is required, and where survey data may involve well in excess of 100,000 households. It is also complicated by the fact that researchers with expertise in CGE modeling are typically different from those with expertise in household modeling. As a result, it can be difficult to satisfactorily integrate the two frameworks into one grand model.

Therefore, we turn next to a series of studies that aim to capitalize on detailed household survey data, while retaining more manageable general equilibrium models. For lack of a better term, we label this category “micro-macro synthesis.” An alternative description might be “general

> 8  Of course, this is still just a sample of _all_ the households in a population.

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Here the dependent variable, , is the total factor productivity of firm in city , industry and country , is a row vector of firm ’s characteristics that may affect productivity (such as firm age and firm size), and is a row vector that captures the local business environment in city and country . We allow for country-specific effects, , which would capture the productivity effects of country-specific factors, including culture and the political system. We also allow for industryspecific effects, . We allow the effects of firm characteristics to differ by country.

For the business environment factors, we include two components. The first is the direct effects of local business environment, captured by the second term on the right hand side of (2). In the specification, we allow the direct effects to differ by country. The inclusion of this term follows the diagnostic approach to growth (Hausman, Rodrik, and Velasco 2005), which emphasizes countryspecific impact of policies. The second component, captured by the third term in (2), includes interactions among the business environment variables. This is again paying heed to the diagnostic approach to growth, and more specifically, to the notion of country-specific bottlenecks (Kremer 1993). Policies often have important indirect effects on other policies, and some bottlenecks may have especially pronounced indirect effects. Those with important positive externality for other policies are likely binding constraints and should receive higher priority in the reform agenda. This literature also notes that it is often difficult to find the interaction effects, perhaps because most existing studies have relied on country-level data, which may not offer sufficient variations. Our investigation of the indirect effects of policies is, therefore, new to the diagnostic approach. We also allow for country-specific coefficients for the interaction terms so that the indirect effects of a policy reform are allowed to differ because of the difference in policy complementarity.

Since the business environment measures, , are observed only once at the end of the sample period, to avoid exaggerating estimation precision, we only use observations in year 2002 for our cross-sectional estimation. Thus, although we have panel data for three years for financial variables, the regression samples consist of the cross-sectional sample of the final year (2002). However, in estimating productivity, we made use of all available data from 2000 to 2002 to improve the reliability of the productivity measures. Since some of the explanatory variables vary only at the city level, we cluster the standard errors at the city level to account for within-city correlation of the error terms and to avoid overstating estimation precision (Bertrand, Duflo and Mullainathan 2004).

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# Figure 4

<!-- Start of picture text --> % Return on Average Assets *<br>- NBH Data -<br>1988 1989 19g0<br>Total 3.4% 4.4% 4.4%<br>Large Banks 3.8% 4.4% 4.3%<br>Smll & Medium Banks 7.0% 7.1% 6.4%<br>Savings Banks 2.3% 3.8% 3.6%<br>Special Finance Inst. 6.2% 3.4% 6.3%<br>SouroCw: National BankofHunary<br><!-- End of picture text --> <!-- Start of picture text --> Leverage: Average Assets / Average Equity *<br>- N8H Data -<br>1988 1989 1990<br>Total 15.0 12.6 12.6<br>LargeBanks 13.5 11.3 12.1<br>Small & Medium Benks 4.3 4.3 5.5<br>Savings Banks 78.4 55.6 36.8<br>Spedal Finance Inst. 2.4 2.7 3.1<br>Sources: Natonal Bank ol Hunga<br><!-- End of picture text --> <!-- Start of picture text --> % Return on Average Equity *<br>- NBH Data -<br>Nominal Real<br>19S8 1980 19 198 1989 1990<br>Total 51.4% 55.0% 54.9% 30.6% 32.4% 20.1%<br>Large Banks 50.8% 49.5% 52.2% 30.0% 27.8% 18.0Yo<br>Small & Medium Banks 30.6% 30.6% 34.9% 12.6% 11.6% 4.6%<br>Savings Banks 176.8% 212.7%h 134.0%o 138.7% 167.2%o 81.4%<br>Special Finance inst. 15.0% 9.4% 19.4% .0.8% -6.5% -7.5%<br>Inflation In CPI 16.0% 17.0% 29.0%<br>Sourcas Nalonal Bank of Hungary<br><!-- End of picture text --> Before IAS adjustments for non-performing loans.

lOa
Putting these equations together, the landlord’s choices are characterized as follows: where d1 = (<sup><u>a</u></sup> b<sup>−c),d2=−c,andσ2=(κm −κs)/b.</sup> Contract choice probabilities can thus be estimated using an ordered logit model, generalized to allow the slope coefficients to vary with the categorical value of the dependent variable.

There are three testable restrictions: d1 < 0, d2 < 0, and d1 − d2 < 0. Intuitively, higher supervision costs affect contract choice by directly lowering the landlord’s return to monitoring a tenant as well as by increasing the attractiveness to him of wealthier tenants, who can be given stronger incentives and concomitantly less supervision. The combination of these two effects, however, operates only at the margin between the MS and S contract, whereas the matching effect alone operates at the margin between the S and FR contract (cf., proposition 1). This fact allows us to identify the ratio of structural determinants of contract choice, i.e.,<sup><u>a</u></sup> b<sup>= d1 −d2,subjecttotheusualnormalizationina</sup> discrete choice model.

# 5.2 Contract choice results

PRHS-01 not only asks landlords about each of their tenants and the terms of their contract, but also about the number of supervisory meetings that they or their kamdars had with their tenant. Thus, we can use the threshold number of annual landlord-tenant meetings (10) estimated in section 4 to construct an analogous supervision indicator on the landlord side. Based on this de fi nition, out of 609 leased plots in our landlord sample, 29% are given on fi xed rent, 25% on ‘unsupervised’ share-contracts, and 46% on ‘supervised’ share-contracts.<sup>22</sup>

> of error in the contract choice equations. The reason for the irrelevance of the proxy relationship here is that, contrary to Ackerberg and Botticini, we do not condition on both observed principal and agent characteristics in the contract choice model.

> 22 Because Sindh is dominated by very large landowners, with a low sampling probability, our subsample of 432 sharecropped plots on the landlord side is weighted toward NWFP (44%) and away from Sindh (25%). By, contrast, 52% of the 351 sharecropped plots in the Table 3 sample are in Sindh, compared to 25% in NWFP. The upshot is that the use of kamdars, largely a Sindh phenomenon, is rarely reported in the landlord sample, and the overall intensity of supervision is somewhat lower than on the tenant side.

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<!-- Start of picture text --> (a) Growth truncated (b) Acceleration truncated<br>6<br>0.6<br>4<br>0.4<br>2<br>0.2<br>0.0 0<br>−0.2<br>−2<br>25 30 35 40 45 50 25 30 35 40 45 50<br>Time Time<br>4 4<br>3 3<br>Levels<br>2 2<br>1 1<br><!-- End of picture text --> Figure 5: MODWT scalogram of the truncated synthetic GDP: (a) growth and (b) acceleration.

and acceleration series by using a truncated version of the data set introduced above. In particular, the new starting point is moved to 25th period of the synthetic data. The original synthetic series have 30 years of growth in the begining (with the exception of 5 years of stagnant GDP in the middle). Therefore, with the truncated data, there is only 5 years of growth in the beginning of the new projection horizon. The figures show that the decomposition by using the truncated data series does not differ from the one with longer series (compare to Figure 4b). This illustrates that the choice of starting period does not affect the decomposition at later periods.

Next, we consider actual data. We use annual series of income, consumption, and investment data from Penn World Tables 7.0, all in 2005 international dollars per capita terms. The income series are provided by the Purchasing Power Parity (PPP) converted GDP per capita (chain series) data, whereas consumption and investment series are computed by using the share of actual consumption and investment in the PPP converted GDP series at 2005 prices.

Decomposition for demonstration purposes are limited to the following five countries: Brazil (BRA), Fiji (FJI), Turkey (TUR), United States (USA), and Zimbabwe (ZWE). The selection of the countries roughly reflects a cross-section of the geographic spread, as well as per capita income levels as defined by the World Bank classification using 2011 Gross National Incomes.

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approach to modeling relative employment changes will predominantly be identifying demand rather than supply effects.

# **3 Data and Descriptive Statistics**

# **3.1 Data and Construction of Key Explanatory Variables**

The Indonesian Manufacturing Census ( _Statistik Industri,_ or SI) contains longitudinal data on all Indonesian manufacturing establishments with more than 20 employees from 1993-2004. A number of features of this dataset render it very suitable for analyzing employer responses to crisis.<sup>10</sup> To start with, a major strength of the data is that it disaggregates firm-level employment into blue and white collar workers by gender. Thus, we can analyze the impact of the East Asian crisis on the employment prospects of men and women, allowing for further differentiation by the broad types of jobs held by men and women. Second and related, the data enable us to study _within-firm_ gender differences in employment controlling for crude occupational category (i.e. differentiating between white and blue-collar workers). Third, by virtue of being a census the data allow us to assess to what extent job losses are driven by exit and reduced entry, and to what extent they are the result of employment adjustment by incumbents. Fourth, the data contain information on the wage-bill for blue- and white-collar workers separately. Following Amiti and Cameron (2007) and Harrison and Scorse (2009), we use this information to construct proxies for firm-level average wages for blue-and white collar workers by dividing those wage bills by number of blue- and white-collar workers respectively. Although these firm-level wage data are not gender-disaggregated, they help us assess gender differences in wage adjustment: following Hellerstein and Neumark (1995, 1999) we focus on the relationship between an establishment‘s average wage in an occupation and the share of female employees in that occupation.<sup>11</sup> Last but not least, the SI data contain information on a rich number of firm characteristics such as their sector, material input usage, output, capital stock,<sup>12</sup> ownership structure and whether or not they export (see appendix A for more details).

> 10 The census treats establishments (plants) rather than firms as their units of interest.  Less than 5% of establishments in the census are owned by multi-plant firms (see Blalock and Gertler 2004 for a thorough discussion).

> 11 Ideally one would control for individual characteristics in testing for differential treatment effects. This information is not available, but we do include firm-level proxies for productivity and wages.

> 12 Information on the capital stock was not recorded in 1996 and missing for some firms in other years. Concerns about possible selection biases have led us not to control for capital per worker in our regressions. The qualitative pattern of

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**Table 8: The Effect of Country Risk on the Level of Investment, by Type of Investment**

|**VARIABLES**|**PPI Green F**|**PPI Green F**|**PPI Concess**|**PPI Concess**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Country Risk|0.0261***|0.0308***|0.0320**|0.0334***|
||(0.00581)|(0.00495)|(0.0133)|(0.0117)|
|Log GDP ppp|1.517***|1.683***|0.770**|0.626**|
||(0.122)|(0.0973)|(0.375)|(0.243)|
|Log GDP Growth|0.108**||-0.0295||
||(0.0551)||(0.143)||
|Log Inflation|-0.131***||-0.124||
||(0.0430)||(0.112)||
|Log Openness|0.428**||-0.444||
||(0.216)||(0.635)||
|Constant|-35.31***|-37.85***|-14.14*|-12.55**|
||(2.653)|(2.341)|(8.158)|(6.196)|
|R2 within|0.2719|0.2618|0.0569|0.0513|
|R2 between|0.6965|0.7070|0.3351|0.2612|
|R2 overall|0.6324|0.6024|0.2918|0.2413|
|Observations|1,088|1,333|318|376|
|Number of countries|113|119|74|80|

*** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations.

# **_5.4 Contrasting public-private partnerships for infrastructure and foreign direct investment_**

In order to illustrate the importance of country risk on the levels of investment in infrastructure, Table 9 contrasts the relationship between the level of private investment in infrastructure and country risk, and the relationship between the level of FDI and country risk. As shown in column (2) of the table, PPI investments are more sensitive to country risk than FDI When the telecom sector is excluded, the effect of country risk is even higher. This is explained by the nature of the telecommunications sector—namely, higher profit margins and faster cost recovery periods--which is more akin to the nature of non-infrastructure sectors of foreign direct investments.<sup>11</sup> Therefore, if we set aside telecommunication investments and reduce our scope to energy, transport and water and sewerage, we can make a more stark comparison between infrastructure investments and FDI.

> 11 As we see in Table 5, the effect of the country risk on the probability to commit investments in the telecom is lower than in the other sector due to the tradable nature of this kind of investments.

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the pre-tax rate of return for domestic private firms was quite high, around 20%, similar to the high rate of return in foreign-invested firms. State firms, on the other hand, had about one-third that rate of return. Employment growth for the private firms has been nearly 10% per year in recent years; employment in state firms has been contracting at 3% per year.

How has China come to have such a vibrant private sector? One factor certainly is the very good investment climate that one finds in many Chinese cities, especially coastal ones. In general, the burden of bureaucracy and regulation is low compared to other developing countries, and the quality of infrastructure very good. This can be seen in some of the indicators from World Bank investment climate surveys carried out in different countries. For example, Chinese firms report that it takes an average of 7 days to get a mainline telephone connection, compared to 34 days in African countries (Table 3). Chinese firms lose about 1% of output to power outages, compared to an average of 4% in African surveys, and much higher numbers in some countries (Kenya, 8%; Tanzania, Uganda, 10%). On the regulatory side, many features of doing business are relatively easy in China: for example, it takes 32 days to register a property, compared to 110 days on average in Africa.

Why have Chinese cities done so well creating a good climate for private investment? Probably one reason is that a key aspect of reform has been to decentralize decision making to provinces and cities. Chinese cities have then competed actively with each other to attract first foreign investment, and more recently, domestic private investment. Among Chinese cities there are significant differences in investment climate, firm profitability, and growth rate of the private sector. Cities that have 50% higher rate 6
The Labor Force Surveys (2007, 2009) contain the necessary information to estimate the size of the informal sector. Following the ILO definition (Hussmanns, 2004), employment is divided into three sectors: (i) agricultural sector (wage or non-wage employment); (ii) informal sector; (iii) formal sector. The informal sector is defined as businesses that are owned by a household or individual, and all other businesses (private sector, state sector, foreign-owned) that do not have a business registration to operate. Although this definition treats the agricultural sector separately, most of the employment in this sector is likely to be of an informal nature.

The LFS surveys show that the share of employment in the informal sector (and agricultural sector) has decreased in the period 2007-2009 for both men and women (Table 9). Women are less likely to be in the informal sector than men, but they are more likely to be in the agricultural sector. The increase in the share of formal sector employment is found for all age groups.

**Table 9: Informal sector employment - %.**

||All|2007<br>Men|Women|All|2009<br>Men|Women|
|---|---|---|---|---|---|---|
|Agricultural employment|50.1|47.9|52.4|47.6|45.4|50.0|
|Informal sector employment|27.7|28.2|27.1|24.4|25.1|23.7|
|Formal sector employment|22.2|23.9|20.5|27.9|29.5|26.3|

Young workers, especially women, are more likely to belong to the formal sector (Table 10). The share of formal sector employment decreases with age especially after the age group 35-44. Young female workers aged 15-24 are more likely than their male counterparts to belong to the formal sector. For all other age groups, men are more likely to belong to the formal sector. This result suggests that as women grow older, get married and have children, they may lose their job in the formal sector or choose to work in the informal sector that provides greater flexibility in working hours. This shows how traditional gender roles affect women’s labor market outcomes.

The increase in formal sector employment has mostly taken place for the younger cohorts (15-24 and 25-34). Combined with the relatively high youth (15-24) unemployment rate, this suggests that youth who are unemployed may be waiting for wage employment in the formal sector.

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This paper aims to empirically examine how the 2008-09 financial crisis affected the level and composition of employment in Central and Western Europe, and how this effect depended on employment protection laws. We focus on European countries because they exhibited substantial heterogeneity in labor market performance during the crisis years (see Table 1), because the availability of harmonized labor market surveys for the region facilitates cross-country comparisons, and as mentioned, because of the continued academic and policy debate on their labor laws.

Using sector (industry) level panel data from 26 countries, our identification strategy adapts the approach in Rajan and Zingales (1998)—hereafter referred to as RZ1998—which is based on the insight that changes in the supply of finance are more likely to affect industries “inherently” more dependent on external financing. The impact of the financial crisis is identified by measuring how the association between aggregate credit and employment during the crisis years varied across industries with high and low inherent financial dependence. Exploiting country and time level variation in the decline in aggregate credit during the crisis years, and industry-level variation in the sensitivity to external finance, this “differences-in-differences” method disentangles the effects of the credit crunch from those of other macroeconomic shocks.<sup>5</sup> It is important to do so because the banking crises in Europe occurred during a global economic slowdown which could have affected labor demand through other channels, such as a decline in export demand.

Our estimates confirm that industries with high financial dependence reduced employment significantly more than their low financial dependence counterparts when aggregate credit conditions worsened. Our baseline estimate suggests that industries in the top quartile of financial dependence

> 5 Specifically, with country, sector and year fixed effect included, our specification is equivalent to a differences-indifferences estimation with a continuous treatment variable (the level of credit decline).

3
|**Appendix Table**|**10: Effe**<br>|**cts of C**<br><br>Lu|**HECC**<br><br>ck|**Preschool**<br>|**, Separate**<br>|**ly by G**<br><br>Merit an|**ender**<br><br>d Luck||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Preschool × Boys||0.07||0.05||0.08||0.07|
|||[0.11]||[0.11]||[0.09]||[0.09]|
|Preschool|-0.11|-0.14|-0.10|-0.13|-0.09|-0.13|-0.08|-0.12|
||[0.05]|[0.07]|[0.05]|[0.07]|[0.04]|[0.06]|[0.04]|[0.06]|
|Boys||-0.02||-0.01||-0.06||-0.05|
|||[0.07]||[0.07]||[0.06]||[0.05]|
|_Demographic Controls?_|N|N|Y|Y|N|N|Y|Y|
|Mean Y among Control Girls|0.35|0.35|0.35|0.35|0.33|0.33|0.33|0.33|
|_Notes_: This table show regressio<br>Observations|ns estima<br>298|ted using<br>298|Cappelen<br>298|et al.’s (2<br>298|20) data “<br>297|Luck” is t<br>297|he chose<br>297|n level of<br>297|

_Notes_ : This table show regressions estimated using Cappelen et al.’s (2020) data. “Luck” is the chosen level of inequality in the luck experiment. “Merit and Luck” is a combined measure of the chosen levels of inequality in the luck and merit experiments. “Preschool” is an indicator for participants randomly assigned to the preschool arm. All regressions include an indicator for participants assigned to the parenting program arm and its interaction with gender (these coefficients are suppressed for ease of exposition). The demographic controls include age and dummies for black and Hispanic. Included but not reported are controls for the time of day when the child took part in the experiment and experimenter fixed effects. We followed all of Cappelen et al.’s (2020) conventions so the estimates can be compared to the results shown in Cappelen et al.’s (2020) Table 3. The odd columns, which do not break by gender, reproduce the results shown in columns (7)–(10) of Cappelen et al.’s (2020) Table 3. Robust standard errors between brackets.

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