rafmacalaba/datause-encoder-data huggingface dataset:
to worry about comparability between HESs and LFSs. It also allows us to test whether the additional durable assets and housing variables (that are not available in the LFS) yield better out-of-sample estimates.

The findings are reported in Table 4. The official poverty estimates are listed in the first column, which shows that poverty has almost halved over the six years’ period from 15.3 to 8.9 percent. Our imputation-based estimates of poverty are able to capture this trend remarkably well. What is equally very encouraging is that the 2001 and 2007 models yield almost identical poverty estimates, despite the six years’ gap, which suggests that the assumption of a time-invariant model is not an unreasonable assumption in the case of Morocco. Finally, what this first test also shows is that the extended model (with assets) does not yield an improvement in poverty estimates. In all estimations with the exception of the 2001 model, the model with assets overshoots more than the model without assets, despite its better in-sample fit.

**Table 4 – Validation Tests Results, within HESs**

|||**No A**|**ssets**|**With**|**Assets**|
|---|---|---|---|---|---|
||Official|||||
|Year|<br>Povert|2001|2007|2001|2007|
||y<br>Estimates|Model|Model|Model|Model|
|2001|15.3|16.0|15.9|16.2|17.1|
||(0.5445)|(0.30)|(0.54)|(0.30)|(0.56)|
|2007|8.9|9.6|9.9|8.8|10.1|
||(0.6089)|(0.27)|(0.34)|(0.28)|(0.45)|

Source: HESs (2001, 2007). Bootstrapped standard errors in parentheses (500 repetitions).

With the second test we impute into the LFS, but still only for the HES years, so that also in this case we are able to compare our estimates to the official poverty estimates. If anything, the imputation-based estimates match the “true” poverty rates even more closely. One possible explanation for this finding is that the LFS is considerably larger than the HES (although note that the “true” estimates are obtained using the HES). It is striking how accurately we are able to estimate poverty in 2007 based on a model estimated using 2001 consumption data, and vice versa.

**Table 5 – Validation Tests Results, cross-survey**

|**Year**|**Official Poverty**<br>**Estimates**|**2001**<br>**Model**|**2007**<br>**Model**|
|---|---|---|---|
|2001|15.3|15.2|15.1|
||(0.5445)|(0.27)|(0.51)|
|2007|8.9|8.4|8.6|
||(0.6089)|(0.19)|(0.28)|

Source: HESs (2001, 2007). Bootstrapped standard errors in parentheses (500 repetitions).

18
discrepancy with higher survey estimates. On average, over three months (97.9 days) pass until administrative reports reach the coverage estimated from our baseline survey data estimates. These lags would have to exceed six months (182 days) on average in our sample of five Sub-Saharan African countries. Of note, they would still have to be substantial even when we account for respondent selection effects in the survey data (38 days in Uganda, 63 days in Burkina Faso, 102 days in Malawi).

# **Denominator issues (under- or overestimating target population size)**

We also find evidence for denominator issues in the administrative data (Table 10). To analyze this, we compare administrative coverage figures, expressed in percent of the total population, between the Our World in Data (OWID) COVID-19 Vaccination data set (the administrative data source for our study, Mathieu et al. 2021) and the WHO’s COVID-19 vaccination dashboard (WHO 2020b). In most cases, the total number of people vaccinated at a given date coincides exactly between both data source. This allows us to compare reported coverage as a total population share for the same date and for the same figure of total number of people vaccinated in the OWID and WHO data. Any discrepancies we detect between vaccination rates (expressed in percent of the population) thus reflect differences in the denominator, that is, the size of the population. We can make this comparison for 19 LMICs.

**Table 10: Potential Numerator and Denominator Issues in Administrative Data**

|**Issue**|**Metric**|**Countries**|**Mean**|**Max**|**Min**|
|---|---|---|---|---|---|
||Average frequency of<br>|36 LMICs|2.4|175|1|
||reporting|5 LMICs in SSA|4.5|175|1|
||Average longest time gap|36 LMICs|54.9|175|1|
|**Numerator Issues**<br>|between two data points|5 LMICs in SSA|90.4|175|42|
|(misreporting of the<br>number of people|Largest jump in<br>administratie data|36 LMICs|0.20|37.3|0|
|<br>currently vaccinated)|v<br>(percentage points)|5 LMICs in SSA|0.20|11.10|0|
||Largest jump in<br>administrative data|36 LMICs|0.11|4.19|0|
||(percentage points per day<br>since last report)|5 LMICs in SSA|0.04|1.33|0|
||Average time lag for admin<br>|22 LMICs|97.9|442|1|
||data to catch up with survey<br>estimate|4 LMICs in SSA|182.6|308|35|
||Average difference in|19 LMICs with<br>||||
|**Denominator Issues**<br>(under- or overestimation<br>of target population size)|reported vaccination rate for<br>identical number of people<br>covered:<br>WHO vs. Our World in|exact match in<br>date and number<br>of people<br>vaccinated|1.6|-2.9|5.1|
||Data|5 LMICs in SSA|1.9|1.4|2.6|

27
# **4 Constraints and Risks Faced by Household Enterprises**

36. Household enterprises face constraints and risks that inhibit their growth and trap them at a low level of productivity. When analyzing them, several important points should be borne in mind. First, HEs differ by location, nature of activity, and characteristics of operators, and may therefore be affected differently by different constraints. While recognizing these differences, it is certain that some constraints are most binding across all HEs. Second, while the study seeks to identify the many constraints HEs face in operating their business, the focus is more on highlighting the underlying differences in how they are affected by these constraints. Third, some operational difficulties faced by HEs are cited as constraints, but may in fact be a reflection of the HE operators‟ weak capacity to do business, the unfavorable business environment in which they operate, and/or their frustration with the government‟s failure to support them. Finally, the analysis of the constraints, risks, and coping strategies of HEs are based primarily on data drawn from the ILFS and the FGD surveys, but also made use of the limited literature available, most notably the studies conducted by Jin and Deininger (2008) and by Lyons and Msoka (2007).

37. Lack of access to credit (hence the lack of capital) was the most often reported constraint by HEs. This is not surprising, as it also applies across the MSME sector (Skof, 2008). Another most commonly cited constraint is the lack of workspace to conduct business. Because of their pervasiveness, these two issues are given greater prominence in the analysis that follows.

## **4.1 Types of Constraints**

## 4.1.1 **_Constraints by Sector/Nature of Activity_**

38. Some of the most binding constraints faced by HEs are specific to the nature of their activity. Business premise, power, and technology constraints are most binding among HEs engaged in manufacturing. HEs in the trading sector contend with constraints imposed by market competition, regulation (or corruption in the regulatory system), and the risk of theft (Table 4.1). How market location, corruption, and lack of workspace adversely affect the hawkers ( _Machingas_ ) will be discussed later in more detail.

**Table ‎4.1:  Ranking of Constraints by Magnitude of Their Effect Across Different Groups of HEs, 2009**

|Constraint|Hawking<br>(_Machinga_on street)|Manufacturing/Skill based<br>(car_penters/h_air dressers)|General Merchandise<br>(kiosk)|
|---|---|---|---|
|Businesspremise|3|1|4|
|Market location/competition|1|4|1|
|Power(electricity)|5|2|5|
|Technology|6|3|6|
|Crime/theft|4|5|3|
|Regulation/corruption|2|6|2|
|**Key issue**|Profitable to look for spot<br>customers,mostlyon street|Earnings are drained by high<br>rentals andpower shading|Located in-house, limited<br>customers,often visited byofficials|

_Note: 1 means most important; 6 means least important. Source: FGD report, 2009_ 20
(2007) to construct weighted country averages.<sup>11</sup> Disaggregated data on firm size and weight by classes are available only for 37 countries. When compared with actual firm distribution by size, we find that weighted and unweighted tax-compliance covary well (correlation of 0.93). The simple average overestimates formality by 1 percentage point across the 37 countries. Weighted and unweighted averages are plotted in figure 6. Correlations between the aggregate measure of formality and a number of countrywide indicators for financial markets are reported in table 6.

# **V. Empirical methodology and basic estimation**

In this section, we present the results of OLS and FE estimation for our main variables of interest. For all of the regressions, we use a simple specification of the type where we regress different measures of access to credit on formality and a large number of basic controls (X) that include categorical dummies for firms size; firm’s age; whether the firm exports (0/1 indicator); whether the firm is foreign owned (more precisely, an indicator taking value of 1 if at least 10% of the firm is owned by foreigners); within country location dummies; industry dummies; and a dummy for whether the manager has some years of college education or more. Note that subscripts _i_ , _k_ and _j_ index respectively firms, industry, and countries. α _j_ and δ _k_ capture country and industry specific intercepts in the fixed effects (FE) estimation.<sup>12</sup> We first measure access to credit as a 0/1 dummy indicating whether the firm has a credit or overdraft line (CREDIT). We report results from OLS and probit to provide benchmark estimates (table 7). However, since OLS and probit estimate coefficients from a combination of within- and between-country variation, these results could be driven by the correlation between better tax compliance and credit availability at the country level. This, in turn, could be the result of the level of institutional development in different countries. To make sure that country-specific

> 11Kozak (2007) compiles data from original census sources and reports shares of micro, small, medium and large enterprises for each country.

> 12 An alternative empirical approach to gauge the impact of informality would be to estimate reduced forms where the TFP productivity residual is regressed on FORMALITY. Although this would provide a more comprehensive assessment of the effects of informality on firms’ performance, using an outcome measure that is based on reported sales (as productivity is) could introduce built-in biases, especially if firms sales reports to the interviewer pattern what is reported for tax purposes (and, hence, FORMALITY).

9
**9**

monopolist to raise his price in response<sup>to a quota so as to appropriate the</sup> entire quota rent, By closing the gap<sup>between the demand price and the supply</sup> price, the monoDolist effectively<sup>strips the licenses of any value. rhis model</sup> with segmented markets is developed<sup>diagramatically</sup> in Takscs (1987) and is mentioned in Shibata (1968) as<sup>well, and mo&c recently in Krugman and Helpman</sup> (1989).

Krishna (1990b, 1990c) further develops<sup>a model in which there is</sup> costless arbitrage between the markets<sup>so the foreign monopolist cannot</sup> 5 practise price discrimination. The monopolist's price is **an** endogenous variable -- by charging a high<sup>price, he can appropriate rants and he chooses</sup> to do so if this is profitable.<sup>Of course, this price depends on the quota</sup> level and his allocation of licenses.<sup>Even here, as long as the license market</sup> is frictionless and competitive,<sup>i~. is still the case .hat the value of a</sup> license equals the difference in<sup>the domestic price and the world price.</sup> However, in this med'el as well as in those of Bhagwati discussed<sup>previously,</sup> thie licen price is endogenous and deper.ds on other oarameters such as the allocation of licenses and the product<sup>market structure and behavior.</sup> To summarize, the existence of markpt<sup>imperfections in general can</sup> result in the non-equivalence of tariffs and quotas. However,<sup>our focus is not</sup> or the equivalence issue but on what<sup>the licence price reflects. In other</sup> words, we are concerned with rent sharing<sup>and not rent appropriation.</sup> When there is product market power on<sup>the seller's side, the license price becQaes</sup> an endogenous variable used by<sup>the producer to effect rent appropriation.</sup> However, the identity continues<sup>to hold that the license price is equal to the</sup> difference between the domestic (demand)<sup>price and the world (supply) price,</sup> so that there is no rent sharing.
incidence of poverty in Mexico varies widely by region. Table 1 illustrates the incidence of poverty in five Mexican regions for the year 2000.<sup>4</sup> Poverty in Mexico is fundamentally a rural phenomenon. More than half of the households living in rural areas are extremely poor. With the exception of the Federal District of Mexico City, northern states are the ones that register the lowest incidence of poverty. The states in the central regions and especially the southernmost states register the highest percentage of poor. While extreme poverty rates are relatively low, especially in urban areas, moderate poverty is more widespread. At the national level more than 50 percent of the population is moderately poor with peaks of about 90 percent in rural areas in the central and southern states. Given these premises, it appears that in order to have the greatest effect on poverty, trade policies need to reach the rural poor in the central and southern regions.

# **2. Exposure of the poor to International Price Shocks**

The extent to which international trade policies will result in a decrease in poverty in Mexico depends in particular upon the exposure of poor Mexican households to trade shocks. The easiest way to think about how poor rural households are affected by trade policies is in terms of the “farm household”, which produces goods and services, sells its labor and consumes goods and services. In this setup, an increase in the price of something of which the household is a net seller increases its real income, while a decrease reduces it.

> 4 The extreme poverty line corresponds to the food poverty line. The moderate poverty line is the asset poverty line. The difference corresponds to non-food components in the consumption bundle. Both poverty lines are calculated by CEPAL (2001).

4
The point to emphasize here is that purchase of Crown lands by private individuals/plantation owners during the late 19th and early 20th century was driven by suitability of land for co¤ee production, a crop which had virtually disappeared from Sri Lanka.<sup>14</sup> The Land Development Ordinance (LDO for short) of 1935 initiated a program of making Government-owned agricultural land available for private household use. The state introduced a system of protected tenure under which recipients of LDO land had the right to occupy and cultivate the land in perpetuity subject to restrictions imposed on sale, leasing and mortgaging, and conditions related to abandoning or failing to cultivate the land. While subsequent amendments have weakened some conditions on mortgage (now allowed for loans from public banks) and limited transferability (with permission from the government), the basic provisions of unitary succession and ban on subdivision and sales of plots and land rental remain largely intact (see Peebles, 2006; De Silva, 1981; World Bank, 2008 for the history of land reforms). Distribution of LDO land took place mainly after Sri Lanka’s Independence in 1948 and much of the land was distributed under various settlement schemes. The settlement schemes brought landless Sinhalese people from the relatively malaria-free south to the historically malaria infested DSDs in the dry zone. The LDO leases today coexist with complete private holding in the same location (World Bank, 2008). The share of land under LDO leases varies signi...cantly across areas in our sample (the maximum is 63 percent and minimum 0.2 percent) which is critical for estimation of the e¤ect of LDO restrictions on equilibrium wages.

# 4. Empirical Strategy

As discussed in the introduction, identi...cation and estimation of the impact of LDO restrictions on wages are challenging because of several reasons. We discuss the identi...cation issues and our approach to solving them in greater details in this section. The most important concern is that the areas with higher percentage of land under LDO restrictions may be de...cient in some other dimensions as well that can in‡uence the marginal product of labor and thus equilibrium wage. The estimated negative e¤ect of LDO incidence may re‡ect these adverse traits in the absence of

> the estate/plantation sector now accounts only for 5.5 percent of Sri Lanka’s population in 2006. Only 8.6 percent of our sample comes from estate/plantation areas. We use a dummy for estate sector in our regressions.

> 14 The co¤ee land (hilly land) are not necessarily considered as particularly suitable for paddy and other ...eld crops which are now the mainstay of Sri Lanka’s smallholder agriculture.

13
Figure 8: Participation by sector and country (Backward and Forward) (2009)* <!-- Start of picture text --> Agribusiness Metals<br>BRA ARG THA IDN CAN MEX TUR ZAF BRA TUR ZAF CAN MEX THA IDN ARG<br>Backward (PART) Forward (PART) Backward (PART) Forward (PART)<br>Automotive Chemicals<br>BRA MEX CAN TUR ARG ZAF THA IDN BRA THA TUR CAN IDN ZAF ARG MEX<br>Backward (PART) Forward (PART) Backward (PART) Forward (PART)<br>8<br>4<br>6<br>3<br>4<br>2<br>1 2<br>0 0<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA. Note: * No updates for 2011.

In all four sectors, Brazil has increased its share on the buying side from China substantially, which is also the case on the selling side for all sectors, except for automotives. The data allow us to distinguish for Brazil the value‐added on both the buying (i.e. importing) and selling (i.e. exporting) sides by country. The results are shown in Figure 9, which plots for Brazil’s most important trading partners the value‐added sold and bought in terms of its overall total gross exports. Figure 9 lays out both the buying and selling sides for Brazil’s value‐added embodied in gross exports for 1995 and 2011. The shares in negative on the left‐hand side of each panel (in blue) represent the buying shares as imports while the number of shares in positive on the right‐ hand side of both panels (in yellow) shows the selling share as exports.

It shows that for agribusiness, Brazil has also increased on the buying side value‐added imports from the US in addition to China, but slightly decreased value‐added on the selling side. In metals, Brazil has increased its share on the buying side from China and the rest of the world while it has increased the share on the selling side to China and Germany. In automotives, it has increased the buying side from Japan and Italy, in addition to China while substantially decreased it from the US. On the selling side in automotives, it has increased value‐added shares to Mexico and Argentina. Finally, in chemicals, in addition to China, Brazil has increased buying value‐added shares from the rest of the world and the US. On the seller’s side of the chemical sector Brazil has increased shares to the US, China and Argentina.

14
<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20%<br>-40%<br>P1 P2 P3 P4 PS<br>H Bottom Decile i Top Decile<br><!-- End of picture text --> <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>5%<br>H Bottom Decile 1 Top Decile<br><!-- End of picture text -->
Table 1: Summary statistics

||N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|_Migration_||||||
|Bilateral migration|6890|4.78|2.27|0.00|12.05|
|_Corruption: Overall variation_||||||
|WGI CC|10912|2.73|1.63|0.11|5.53|
|WGI GE|10912|2.59|1.16|0.50|5.72|
|CPI|10571|3.43|1.65|0.60|6.70|
|ICRG|8649|3.63|1.95|0.00|6.67|
|_Corruption: Between variation_||||||
|WGI CC|992|-|1.61|0.38|5.43|
|WGI GE|992|-|1.14|0.87|5.43|
|CPI|961|-|1.61|0.88|6.05|
|ICRG|961|-|1.90|0.43|6.47|
|_Corruption: Within variation_||||||
|WGI CC|11|-|0.23|1.95|4.07|
|WGI GE|11|-|0.22|1.97|3.26|
|CPI|11|-|0.33|2.65|4.54|
|ICRG|9|-|0.45|1.95|5.66|
|_Gravity_||||||
|Common border|9570|0.09|0.29|0.00|1.00|
|Time zone diference|9570|0.67|0.64|0.00|2.50|
|Distance (_log_)|9570|7.15|0.61|5.08|8.49|
|Size of the country area (_log_)|9570|11.22|1.56|5.76|13.21|
|Common language|9570|0.05|0.21|0.00|1.00|
|Common religion|9570|0.29|0.30|0.00|0.94|
|Common currency|9570|0.30|0.46|0.00|1.00|
|_Additional controls_||||||
|GDP per capita PPP (_log_)|10571|10.58|0.38|9.75|11.64|
|Infation (_%_)|10633|1.76|2.38|-9.90|16.02|
|Agriculture value added|10633|2.19|1.31|0.12|6.60|
|Services value added|10633|63.40|6.69|43.15|79.33|
|Right to work|10912|0.93|0.25|0.00|1.00|
|Social protection exp. (_%_ GDP)|8866|22.46|4.68|13.02|32.21|
|Product market regulation|8184|1.64|0.49|0.54|3.12|
|EPL: Individual dismissals|8587|2.28|0.64|1.10|4.42|
|EPL: Temporary contracts|8587|1.70|0.84|0.38|3.75|

WGI CC and WGI GE are the Worldwide Governance Indicators Control of Corruption and the Worldwide Governance Indicators Government Effectiveness, respectively. CPI is the Corruption Perception Index and ICRG is the International Country Risk Guide.

15
Policy Research Working Paper 7222

# **Abstract**

Female labor force participation rates in urban India between 1987 and 2011 are surprisingly low and have stagnated since the late 1980s. Despite rising growth, fertility decline, and rising wages and education levels, married women’s labor force participation hovered around 18 percent. Analysis of five large cross-sectional micro surveys shows that a combination of supply and demand effects have contributed to this stagnation. The main supply side factors are rising household incomes and husband’s education as well as the falling selectivity of highly educated women. On the demand side, the sectors that draw in female workers have expanded least, so that changes in the sectoral structure of employment alone would have actually led to declining participation rates.

This paper is a product of the Development Policy Department, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sklasen@uni-goettingen.de and janneke.pieters@wur.nl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team