rafmacalaba/datause-encoder-data huggingface dataset:
theories focus on the outcomes and sociological theories also explain why those outcomes obtain.

_At the core of labor market segmentation are social groups and institutions. The processes governing allocation and pricing within internal labor markets are social, opposed either to competitive processes or to instrumental calculations. The marginal labor force commitment of the groups which creates the potential for a viable secondary sector of a dual market is social. The structures which distinguish professional and managerial workers from other members of the labor force and provide their distinctive education and training are also social. - Piore, 1983:252_ Taking forward Piore’s (1983) conception of the labor market being a social entity, a strong body of literature focuses on the role of norms, values and culture in determining labor market outcomes and processes. For instance, most economic theories distinguish between demand for and supply of labor, but this paper argues that once you take into account the effect of macro level norms, the distinction between demand and supply of labor gets blurred. Take the case of women’s market work. That norms operate at multiple levels, permeating the household as well as the public spheres is now well recognized (World Bank 2012). So, discrimination as well operates at, at least two levels - the macro level, when institutions discriminate, such as when unfair laws are passed and at the micro level when individuals are not fairly treated or compensated.

Take the case of Muslim women’s employment in most countries with large proportions of Muslims. Despite considerable heterogeneity across the world, the practice of veiling and norms of physical segregation of sexes prevalent in Muslim societies, are often implicated in the low labor force participation rates of Muslim women. Hiring authorities in India for instance, sometimes bemoan the low numbers of female Muslim applicants to public sector jobs, but Das (2004) in an analysis of Muslim women’s employment using national labor force data and qualitative interviews points out that cultural norms operate in a complex manner. In fact, hiring authorities in some countries have a strong role in reinforcing 6
sample size of the uninterested firms, coupled with lower response rates, however makes the confidence intervals on sales and profits reasonably wide, encompassing a change of 15 percent in either direction.

# **5. What does this experiment tell us about the nature of firm competition?**

Our data provide a rare look into the pricing of multi-product small retailers of perishable products in a developing country, and enable us to establish several key facts that are important for understanding how firms compete with one another in these markets, and how firms charging different prices to nearby firms are able to survive over time.

# **5.1 Facts about price changes in these markets**

_Fact 1: Prices change frequently and closely follow the Corabastos wholesale prices._ As noted above, 76 percent of firms at baseline say that they change prices daily. Our six-month follow-up survey asked several questions about how they set prices. Only 9 percent of firms say they know the sale prices of fruits and vegetables at the nearest shop to theirs in the neighborhood, and only 4 percent say they aim to set a price explicitly below that of a competitor. Instead, 91 percent of firms say they just add a mark-up to their purchase price.<sup>19</sup> The result is that retail sale prices closely track vendor purchase prices, which in turn closely track the prices charged at Corabastos. This is seen in Figure 5.

In a simple model of perfect competition, there should be one-for-one pass-through of market prices into retail prices. In contrast, if retail store owners have market power, then they will not pass-through cost changes automatically to consumers. In Table 7, we regress the daily sale prices of our core five Agruppa products charged by retail store owners on the daily price at Corabastos and on block fixed effects. The coefficients range from 0.9 to 1.2, and are thus close to one in magnitude, showing high pass-through of input price changes, and suggesting a high level of competition among firms. Note that with imperfect competition, the degree of pass-through is a function of both the level of competition and the curvature of the demand curve (see FalcaoBergquist, 2016), so that high pass-through per se is not sufficient to establish perfect competition.

> 19 However, note that firms do not use the same mark-up consistently over time (or across products): the average rankorder correlation between the mark-up used for a product today and the mark-up used 2 weeks later is only 0.29, and it is 0.13 with the mark-up used one year later. This shows that mark-ups do not reflect persistent unobserved differences in firm quality.

21
combination of substantial income growth in rural areas, albeit with higher levels of rural inequality (World Bank 2004). During 1999-2002, the incomes of the poor population grew in Mexico, despite economy-wide stagnation (Appendix B). Two factors drove this, namely rapid growth in rural labor incomes of workers and self-employed, with some reduction in the wage premia to higher levels of education and large contribution from remittances and government transfers (Appendix B).

The income of the extreme poor households in rural areas is stagnant (Table 3.2). Average per-capita household income decreased from 1992-96 and increased during 1996-2002, but in 2002 it had still not return to the 1992 level. In 2002, the average income of the extreme poor rural households (P$315.5) was less than a third of the average income of the non-poor in rural Mexico.

**Table 3.2: Average per-capita income by poverty level,** **<u>Mexico, selected years 1992-2002</u>**

||**199**<br>|**2**<br>|**199**<br>|**6**<br>|**200**<br>|**2**<br>|
|---|---|---|---|---|---|---|
|**Region**|**Non-poor**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|**Poor**|
|**Dispersed Rural**<sup>**1**</sup>|1,277.76|329.29|1,163.56|302.80|1,417.20|315.45|
|**Semi-urban**<sup>**2**</sup>|1,600.69|346.55|1,304.62|349.91|1,521.37|327.74|
|**Urban**|2,940.99|520.70|2,470.30|511.12|2,647.41|506.64|
|Source: ENIGH 19<br>|92-2002.<br>||||||
|1Rural: Localities<br>2 Semi-urban:loca|with less than<br>lities with 2,5|2,500 inh<br>00 to15,0|abitants.<br>00inhabitant|s.|||

Rural household heads with no income decreased during 1992-2002 (Table 3.3). In 2002, 4.2 percent of household heads had no income, down 1.3 percentage points since 1992 and 0.8 percentage point since 1996. Two factors may have played a role in reducing the share of rural households with no incomes: _first_ , increased education attainment in rural areas, and _second_ , a reduction in the share of inactive households heads in rural areas.

12
Wealth will be measured by whether the families possess any land since about 40 per cent of the households are landless. To measure the human capital of the husband and wife we use their years of schooling and their ages at marriage. Alternatives in the marriage market are measured by the date of the marriage (to capture exogenous trends), and the ratio of the number of women to the number men at marriageable ages (defined as women aged 10-19/ men aged 2029) measured at the year of the marriage for the bride's home district. In addition to these variables I also include dummies for whether the family is Muslim or belongs to a disadvantaged or "Scheduled" caste.

# Data and Results

The sample consists of 800 households randomly chosen from five districts spread across North and South Karnataka State. Seven villages from each district were randomly chosen and twenty households then randomly selected from each village. The marriage data that are employed in this paper were collected retrospectively from the married women in the sample. About a third of the sample did not have any ever-married women of reproductive age and therefore did not answer the women's questions. Of those that did have eligible women, many did not provide complete answers to the retrospective marriage questions, forcing us to drop them from the analysis. After eliminating about ten outliers, we are left with a sample of about 300 women spread across the five districts. There are no significant differences between this subsample and the complete sample for those questions that were answered by all the households.

Table 1 reports summary statistics from the survey data. The average wedding celebration expenses are about 3,000 rupees, 11 per cent as large as the average dowry which is 26,000 rupees. Average schooling levels for men are 4.3 years and 2.9 years for women. The average age at marriage for men is about 25 years and 17 years for women. The mean marriage took place in 1980, with a standard deviation of 8.4, showing that the marriages span a fairly long time. Over this period, the average marriage ratio is 1.13 indicating a surplus of women in the marriage market. About 9 per cent of the sample is Muslim and 22 per cent belong to scheduled castes. Finally, 68 per cent of the households customarily practice village exogamy.

Table 2 reports estimates for the wedding celebration OLS regressions. Looking at the regression with no interactions, we see that grooms with land have celebrations that are statistically significant and about 1,400 rupees higher than landless grooms. Families that belong to sub-castes that practice village exogamy have marriage expenses about 1,500 rupees higher than those who marry within the village. The results of the interaction effects between village exogamy and the wealth and education of the husband are also consistent with the hypothesis 9
enables us to explore multiple hypotheses within social protection schemes from the Nigerien setting. We discuss the results in light of social protection schemes for Niger. The overall findings have implications for social policy and broader issues of poverty in Niger, with potential extensions to other developing countries.<sup>4</sup> The paper is related to the broader literature on public work programs, which examines their effects on a vector of outcomes such as consumption stabilization (Ravi & Engler 2015,); food security (Gilligan, Hoddinott & Taffesse 2009; <mark>Kathleen, Galasso and Goldberg</mark> 2015); investments in both agriculture and assets (respectively, Hoddinott et al. 2012; and Stoeffler, Premand & Mills 2014); employment and wages (Imbert & Papp 2015; Deiniger & Liu 2013); and more recently, resilience and adaptability to shocks (McCord, Beazley, Solorzano & Artur 2016; Handa & Otchere 2017). First, the evidence on effects is mixed; for example <mark>Kathleen, Galasso and Goldberg</mark> (2015) document that Malawi’s SAF failed to improve food security (a primary aim of the program), while Gilligan, Hoddinott & Taesse (2009) and Berhane et al. (2014) respectively find modest and significant improvements in food security for Ethiopia's PSNP. Second, most of this literature has focused on investigating specific social programs; for example India’s NREGA, Ethiopia’s PNSP, Malawi’s SAF, and Mozambique’s PASP programs.<sup>5</sup> We extend this literature by (1) attempting to examine the combined cash and noncash social programs in Niger captured in national surveys—possibly, accounting for general equilibrium effects (Muralidharan, Niehaus & Sukhtankar 2016)—and then (2) formulating a tractable framework that provides an alternative for assessing the concept of household resilience.

Finally, this paper is also connected to the literature on design of social programs, exploring the role of gender and spatial dimensions for targeting (Abril and Ofosu-Amaah 2009; Hasan 2010).

> 4 **Data Sets** : Our analyses rely on panel data collected in Niger between 2010/11–2014/15. Occasionally, we draw on secondary evidence and case studies from other contexts where necessary. The panel data come from joint data collection efforts between the World Bank Group WBG and the National Statistical Office NSO of Niger. The data are rich in content, where in each year two waves of data collections are carried out. Information about several variables are contained in these data sets –broadly spanning individual households, agriculture and livestock holdings, and community level records. **Econometric Models** : Throughout, all discussions about our empirical model and identification strategy are relegated to the Appendix. Supplementary results are also provided in the supplemental figures section of the Appendix.

> 5 PASP denotes Productive Social Action Programme.

‐ 4 ‐
|ional Study<br>**Trade and**<br>**liberalization**<br> **are generally**<br>**applicablea**<br>✓<br>✓<br>✓<br>✓<br>_continued_|
|---|
|and Conclusions of a Major Reg<br>**isks and limitations**<br>**International Market Policy**<br>**commitments failures**<br>**conflict **|
|ry Analysis<br>**R**<br>**Rights and**<br>**trans. costs**|
|d Format for Count<br> **Typical bands**<br>Numerous<br>narrow bands<br>up to 16 GHz<br>Numerous<br>narrow bands<br>up to 259 GHz<br>12 GHz. Up-<br>links 14 and<br>17 GHz<br>Radio 0.5–26<br>and 88–108 MHz.<br>Analog and<br>digital TV<br>470-854 MHz<br>Throughout<br>spectrum, usually<br>shared<br>2.5, 10, 26,<br>28 GHz<br>0.9, 2.5, 5 GHz|
|uggeste<br>**Nr of**<br>**users **<br>+++<br>+++<br>+<br>+<br>++<br>+++<br>+++|
|ope for Spectrum Trade and Liberalization: S<br>**Summary description**<br>Radio services for aircraft. Includes<br>aeronautical navigation, traffic control,<br>communication (incuding satellite), global<br>positioning.<br>Amateur radio: experimentation, community<br>support, leisure. Citizen band radio: short-<br>range personal and business communication.<br> Downlink radio and TV broadcasting from<br>satellites to community or individual<br>receivers. Associated uplinks to feed<br>programs.<br> Radio and TV broadcasting to the general<br>public, either commercial or as a public<br>service.<br>Terrestrial point-to-point links<br>(e.g., microwave), generally part of<br>networks for corporate or public<br>communication services (e.g., between<br>mobile base stations)<br>Systems to connect commercial or<br>residential locations to public networks.<br>Includes wireless local loop, broadband<br>Internet access.<br>ISM: Industrial, scientific, and medical<br>non-commercial uses. Also wireless local<br>area networks, cordless phones, alarms,<br>medical implants, and other short-range<br>devices.|
|**Table 3.2**<br>Sc<br>**Spectrum uses**<br>Aeronautical<br>Amateur radio,<br>CB<br>Broadcasting— <br>satellite<br>Broadcasting— <br>terrestrial<br>Fixed links<br>Fixed wireless<br>access<br>ISM and short-<br>range|

**25**
procyclicality. As a result, on average, fiscal policy amplifies the business cycle by 21.4 percent. The good news is that implementing/deepening certain institutions, including fiscal rules overseen by independent fiscal councils and sovereign wealth funds, should help in reducing procyclicality.

The paper proceeds as follows. The next section will present a set of stylized facts that will serve as a general framework to interpret the subsequent empirical and numerical analysis. Section 3 carries out the econometric testing of different theories to explain fiscal procyclicality. Section 4 tests the significance of institutional variables. Section 5 provides the numerical analysis of the pours component. Section 6 computes the welfare costs of fiscal procyclicality. Finally, Section 7 closes the paper with policy implications and directions for further research.

# 2 Stylized facts for commodity exporters

This section develops a set of stylized facts for understanding procyclicality in commodityexporters that will provide the foundations for the more formal econometric/quantitative analysis in the rest of the paper. We first look at some basic facts on fiscal procyclicality in CE, and then turn to macroeconomic and institutional variables expected to have an impact on the degree of procyclicality.<sup>7</sup>

## 2.1 Fiscal procyclicality

As shown in Figure 1, over the past forty years EMDE have been strongly procyclical while AE have been acyclical. But what has happened in commodity-exporting countries compared to non-commodity-exporting countries? Figure 2 shows that, while both groups of countries have been procyclical, the correlation between the cyclical components of real government spending and real GDP for CE is twice as large as that for non-CE (0.43 compared to 0.22, both highly significant).<sup>8</sup> 9 In fact, this result continues to hold when we look at the three major commodities categories (i.e.,

> 7Throughout this analysis, we rely on the Hodrick-Prescott filter. The results are robust to the use of alternative filters, such as Baxter-King (see Appendix 9 for details), or non-parametric filters, as shown by Kaminsky et al. (2004) and Carneiro and Garrido (2016).

> 8Henceforth, “highly significant”will be taken to mean significant at the one percent level.

> 9For commodity exporters, we use the World Bank definition.

6
financial institutions only for medium-sized and large enterprises. And larger low-end financial institutions might actually be better in easing access to finance in low-income countries.

Before proceeding, an important caveat is due. Our results derive from cross-sectional variation across countries and although we control for an array of firm and country characteristics, we can therefore not completely exclude the possibility of omitted variable bias. We mitigate this concern, however, by testing for the differential relationship between financial structure and average size of financial institutions, on the one hand, and access to external finance by firms in countries at different levels of GDP per capita, firms of different sizes and firms in industries with different financing needs. It is important to stress, however, that we do not interpret our findings as causal relationships.

The remainder of the paper is structured as follows. The next section discusses the data sources and variables we use. Section 3 presents methodology and section 4 our results. Section 5 concludes.

# **2. Data**

We use data from two main sources to construct our sample. We use the Financial Sector Assessment Program (FSAP) reports, which are jointly prepared by the IMF and World Bank<sup>4</sup> , to construct our measures of the importance and average size of different segments of the financial system and firm-level data from the World Bank’s Enterprise Surveys to measure firms’ access to and use of financial services. Since there is limited overlap between the two datasets, we end up with a total of 54 sample countries and up to 50 countries per regressions. All our countries are developing or emerging countries, with 19 countries in Europe and Central Asia, 10 countries in Latin America, 23 countries in Sub-Saharan Africa, and 2 countries in East Asia and Pacific. The level of economic development, as measured by GDP per capita (in constant 2000 USD), varies significantly across our sample countries, ranging from 134 USD in Malawi to 7,229 USD in Uruguay.

Established in 1999, the FSAP is a comprehensive and in-depth analysis of a country’s financial sector. Historically, full FSAP updates take place about every four to seven years in any

> 4 To be exact, FSAP is a joint undertaking of the World Bank and the IMF in developing and emerging market countries and of the IMF alone in advanced economies.

5
# 3.1 Effects of Size and Age on the Hazard Rate of Exit

Size and age are two important observable characteristics of establishments that have been extensively used to analyze their dynamics. Many studies have shown that the hazard rate of exit decreases as size and age increase<sup>10</sup> . Figure 1 shows the relation for the Chilean dataset including all industries. Each line represents a size cohort where size is measured as total sales. As both size and age increase, the hazard rate of exit decreases. The establishments in the smallest size cohort face higher exit rates than the other cohorts and the decline in hazard rate as age increases is slower in this size cohort. This is probably due to the small number of observations in the older ages for that size cohort. For the other cohorts, decrease in the hazard rate of exit is more pronounced.

Figure 1: Hazard Rate of Exit Conditional on Size and Age (Data) <!-- Start of picture text --> 0.29<br>S<=200<br>0.24<br>0.19<br>Hazard 200<S<=300<br>Rate<br>0.14<br>300<S<=1000<br>0.09<br>S>1000<br>0.04<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Age<br><!-- End of picture text --> The Klette and Kortum (2004) model can explain the relation between the hazard rate of exit and size. It also generates the negative relation between age and the hazard rate of exit but only because age is a proxy for size. Conditional on size, age has no effect on the hazard rate of exit. Introducing random sized products, Klepper and Thompson (2007) can explain both relations independently. In my model, the relation holds for a different reason. Establishments with different efficiency levels produce different numbers of products. Given size, there is a mixture of producers with different number of products and the older ones are more likely to

> 10Caves (1998) reviews the empirical literature on these relations.

13
# 1. Introduction

Economic growth in the Latin America and Caribbean (LAC) region has slowed significantly since the start of the decade. While there is evidence of an uptick in growth since 2017, driven largely by external factors such as improved commodity prices (Vegh et al., 2018), recent projections forecast an annual growth rate of 1.9 percent for 2018, which is below that of the region’s major trading partners—the US and EU, and indeed is below the global average. If this pattern continues going forward, the region’s share in the global economy is forecast to decline. What is at the root of this problem? Low productivity growth in addition to low levels of capital accumulation have been highlighted as the principal explanations for the region’s poor economic progress (see, for example, Crespi and Zuniga (2012), Pagés (2010) and IDB (2018)). Indeed, the possible causes underlying low productivity in the region are numerous; a recent report by the Inter‐American Development Bank (IDB, 2018) notes that factors such as tax administration, labor regulations, and a lack of innovation may all contribute to the problem. What kind of policies could be implemented to boost productivity? While regulatory and institutional reform have an important role to play, in this research we consider policies at the firm level. In the past decade, a growing body of evidence has emerged, pointing to the strong impact that improved management practices can exert on firm‐level productivity. It is this link—that between management practices and productivity—that we focus on in this paper, using data from recent Enterprise Surveys of seven countries in the region, namely Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru and Uruguay.

While acknowledgment of the link between management practices and productivity has been gaining ground in the past decade or so, the concept is not new. Indeed, as far back as the mid‐1980s, Gershenberg (1986) noted a shift in focus among policy makers and economists away from the roles that only capital and labor may play in increasing output in less‐developed economies, and towards the recognition of the role that management may play in boosting productivity. Much of the more recent research in the area has been pioneered by Bloom and Van Reenen. As outlined in their seminal paper on the topic (Bloom and Van Reenen, 2007), the authors developed a survey instrument to systematically measure management practices of 732 medium‐sized manufacturing firms in France, Germany, the United Kingdom and the United States. Their results show that better management practices are associated with higher productivity, profitability and firm survival. Another interesting result that emerges from their analysis is that the management practices of firms in the United States are superior to those in the other countries. However, in all countries there is significant heterogeneity among firms, with a tail of 2