rafmacalaba/datause-encoder-data huggingface dataset:
declined, as did the severity of poverty, as measured by the poverty gap and squared poverty gap.

Even after making adjustments for the inconsistency of Ugandan labor force data over time, we still find an overwhelming majority of the workforce reporting agriculture as their primary economic activity in the most recent household survey (see Table 1).<sup>8</sup> Indeed, it appears that the Uganda‘s high rate of non-agricultural economic growth allowed only a very small part of the labor force to move into higher value activities. Are these numbers a true reflection of the employment transition? First, Uganda has added 7 million people (i.e. doubled) into the labor force since 1992, and at least 2.7 million (i.e. 40%) of these eventually found primary employment outside the agricultural sector. So the slow demographic transition hides some of the structural shifts. Second, since the first data were collected in 1992 the Ugandan basic multipurpose household survey instrument (UNHS) has been modified, with the improvements designed in part to sweep more people into the labor force. Many of those left out in 1992 worked primarily in agriculture. But the result of the changes makes it impossible to get a consistent series on employment in Uganda. Our estimates attempt to correct for this, but still show a less than expected net flow out of agriculture as a primary source of employment given the extent of the economic transformation suggested by the sectoral GDP numbers, and the high rate of poverty

> 8 The labor module used in Uganda‘s National Household Surveys (UNHS) has been changing since 1992 leading to problems of comparability across surveys. Major changes include the introduction of screening for economic participation in 2002/03 (with short screening) and 2005/06 (with detailed screening) and varying recall periods for main and secondary employment statuses. The level of details on employment statuses also change, with the latest survey (UNHS 2009/10) missing secondary employment in the 12 months recall and thus limiting the usefulness of that data for livelihood analysis. Consequently this paper uses the UNHS 2005/06 as the latest survey whenever secondary employment or a complete livelihood analysis is required.  Measurement of employment status and economic participation is done only with surveys cases where these variables can be constructed in a comparable way e.g. by building from main employment status questions and household enterprise modules using similar recall periods.

14
with a lack of evidence around their impact on firm performance, this paper contributes to a largely unexplored empirical literature. It does so thanks to a novel and unique dataset that includes detailed information on firms both within and outside economic zones across Bangladesh, Bhutan, India, and Nepal. It employs standard econometric techniques in the empirical literature on estimating the impact of zones on firm performance.

The first main finding of the paper is that being inside a zone has a positive and significant effect on FDI and employment. This lends support to the idea that when governments are unable to rectify constraints at an economy-wide level, addressing challenges within discrete areas can be a viable alternative to support firm performance.

The second main finding is that the effect of zones is mixed across zone types. Industrial areas and estates show significant and negative effects on exports and employment. Conversely, industrial parks have positive effects on FDI and employment, while EPZs demonstrate the most consistent positive effects across all measures of firm performance.

The third main finding is that controlling for zone status, public support services have significant impacts on firm performance. In particular, infrastructure support and trade and connectivity facilities have the most consistent positive effects across measures of firm performance. Governance and fiscal incentives have positive effects only on the measures of employment and exports, respectively. These findings further underscore the notion that policymakers need to worry about the design and implementation arrangements of zone programs.

Finally, we explore the effects of zone status on firm growth. Zone status is positively and significantly associated with younger firms and EPZs have a positive effect on employment generation.

In terms of future research, the heterogeneous performance of zones raises questions about why EPZs overall seem to improve firms’ competitiveness and efficiency, and why some zone types produce negative or no discernible effects on firm performance. Given the range of possible government interventions delivered through such programs, and the relative importance of infrastructure and trade facilitation, it seems that the success of zone programs depends largely on providing the “right” support services that address legitimate obstacles to firm operation and growth. This has merit whether inside the walls of a zone or outside. Additionally, the question arises whether localized programs might hinder the 16
rudimentary markets in capital, labor, land, technology, communication, and other factors of production (Yeung, Lee, and Kee 2009).

The initial opening to trade and investment having proved successful, China resolved to open its economy further. In 1984, the central authorities created a variant of SEZs, which they dubbed economic and technological development zones, informally known as China’s national industrial parks. The difference between the comprehensive SEZs and the ETDZs is one of scale. A comprehensive SEZ often consists of a much larger area (sometimes an entire city or province). From 1984 to 1988, 14 ETDZs were established in additional coastal cities<sup>3</sup> and in the following years in cities in the Pearl River Delta, the Yangtze River Delta, and the Min Delta in Fujian. Meanwhile, in 1988, the entire province of Hainan was designated as the fifth comprehensive SEZ, and in 1989 and 2006, Shanghai Pudong New Area and Tianjin Binhai New Area were granted such status as well.

Subsequently, in 1992, the State Council created another 35 ETDZs. In doing so, they sought (a) to extend the ETDZs from the coastline to inland regions and (b) to focus less on fundamental industries and more on technology-intensive industries. By the end of 2008, there were 54 state-level ETDZs. By April 2010, this number increased to 69: 18 in the Yangtze River Delta, 10 in the Pearl River Delta, 15 in the central region, 11 in the Bohai Bay region, 2 in the northeast region, and 13 in the western region (see map 1.1). ETDZs are typically located in the suburban regions of a major city. Within the ETDZ, an administrative committee, commonly selected by the local government, oversees the economic and social management of the zone on behalf of the local administration (China Knowledge Online 2009).

In addition to the special economic zones mentioned above, other types of SEZs in China include high-tech industrial development zones (HIDZs), free trade zones (FTZs), export-processing zones (EPZs), and others. Each has a different focus.

**_High-tech industrial development zones._** The establishment of high-tech industrial development zones was to implement the Torch Program initiated by the Ministry of Science and Technology in the late 1980s. The main objective of the program was to use the technological capacity and resources of research institutes, universities, and large and medium enterprises to develop new and high-tech products and to expedite the commercialization of research and development (R&D).

10
countries are taking action towards improving older issues that needed revisiting (such as deposit insurance or the provision of guarantees) or are more prone to strengthen the implementation of policies in areas where lower income countries have less resources to focus on (such as AML).

The top fifteen intermediates (Table 9) include a number of relatively sophisticated items of infrastructure, oversight or public policy such as auditing standards, extra-judicial resolution arrangements, the oversight of insurance companies, pension funds and capital markets, and the management of public debt. However, it also includes items that one would expect to be rather more basic, such as key components of the informational framework (accounting standards and the strengthening of debtor registries).

Finally, as regards the basic recommendations (Table 10), not surprisingly many key items of infrastructure are high in the list, including the judiciary (specialized courts and judiciary transparency) and the more basic aspects of information storage (introduction of debtor and company registries). As regards oversight, microfinance comes at the very top, which is again hardly surprising. Basic recommendations on public policy include some of the key building pieces for monetary and foreign exchange management (liquidity forecasting, the institutional framework for monetary policy), and financial development policies (including the closure of development banks and the reform of social security systems).

**Table 8.  Top Fifteen Advanced Recommendations**

|||**HIC**|**MIC**|**LIC**|**Score**<br>**9**|
|---|---|---|---|---|---|
|1|_Early warnings - Banking - Crisis Management_|0.3|0.3|0.1|2.1|
|2|_Institution based risk management - Banking Regulation_|1.9|0.5|0.3|1.6|
|3|_Capital Markets – Supervision_|1.3|0.8|0.6|1.4|
|4|_Supervision and Enforcement - AML_|1.2|0.4|0.1|1.3|
|5|_Global Oversight – Supervision_|5.6|2.7|1.4|1.1|
|6|_Capital Markets – Regulation_|2.9|1.2|0.9|1.1|
|7|_Coordinated Oversight - Global Oversight - Regulation_|0.7|0.2|0.1|1.1|
|8|_Investment Funds - Capital Markets - Legal_|0.3|0.3|0.2|1.0|
|9|_Coordinated Oversight - Global Oversight Supervision_|1.9|0.8|0.1|1.0|
|10|_Shareholders - Corporate Governance_|0.8|0.5|0.1|0.9|
|11|_Insurance – Supervision_|4.0|2.4|1.5|0.9|
|12|_Crisis Contingency Planning - Resolution - Banking Crisis Management_|1.0|0.5|0.2|0.8|
|13|_Modification - Deposit Insurance_|1.0|0.8|0.1|0.8|
|14|_General Legal & Institutional Framework - Capital Markets_|2.0|1.3|0.8|0.8|
|15|_Guarantees-Indirect Intervention_|0.3|0.3|0.1|0.8|

> 9 The score is derived from average income elasticities obtained from a regression in which both the level and the square of GDP per capita are introduced as explanatory variables.

17
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# **Table 8. Welfare Outcomes**

||Percent of|Log total<br>|Food<br>|Welf|<br>are standardize|<br>d index|
|---|---|---|---|---|---|---|
||output sold|household<br>income|security<br>PCA index|Full sample|Baseline<br>sample|Matched<br>sample|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Beneficiary|0.249***|-0.594|0.403**|0.288**|0.290|0.314|
||_0.001_|_0.145_|_0.033_|_0.026_|_0.121_|_0.233_|
|Lottery|0.162|0.257|0.318|0.220*|0.458**|0.520**|
||_0.128_|_0.500_|_0.223_|_0.090_|_0.012_|_0.025_|
|Lottery x Beneficiary|-0.058|0.744|-0.281|-0.131|-0.237|-0.319|
||_0.544_|_0.117_|_0.282_|_0.413_|_0.338_|_0.307_|
|R-squared|||0.010|0.018|0.037|0.032|
|Mean of dep. var. for NB in NL vil.|0.125|12.583|-0.758|-0.199|-0.320|-0.340|
|P-val of B = NB in L villages|_0.000_|_0.525_|_0.552_|_0.118_|_0.759_|_0.981_|
|Model|Tobit|Tobit|OLS|OLS|OLS|OLS|
|Percentage of censured observations|0.597|0.038|||||
|Number of households|828|547|920|828|381|299|

Note: This table reports the estimation of the following specifications: _y ij = α ij + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here are not adjusted for any baseline covariates. Column 1 reports a percentage of production that is sold. Column 2 reports on log of total household income, including non-agricultural activities. Dependent variable in Column 3 is constructed using the households reported worry for scarcity of food, number of meals per day, size of meals, and consumption of least preferred foods; higher values represent higher food security. The dependent variable in all columns 4-6 is a standardized index of agricultural outcomes and it was constructed base on questions asked on follow-up household survey. The index components are (i) percentage of production that is sold and (ii) food security index, where higher values represent higher food security. In Column 4, we use the sample of households interviewed at follow-up with non-missing values of the dependent variables in Column 1 and 3. In Column 5, we restrict the sample to households with baseline and endline data. In Column 6, we use the matched sample, that is, we restrict the sample in Column 5 to beneficiaries and non-beneficiaries with the same common support based on a nearest neighbor propensity score matching algorithm. P-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008) and reported below the coefficients. Clustering was done at the village level. ***,**,* denote statistical significance at the 1, 5 and 10 percent level, respectively. See Appendix Table 1 for definition of variables.
where θ _hs_ , _u_ denotes the share of income from skilled or unskilled labor, while θ _hgx_ is the income share from the sale of good _g_ and θ _hgc_ denotes budget shares for good g. The parameters _dwrs_ , _u_ are retrieved from the estimation of equation (3.15) and discussed in section 4.2. The parameter _dpgr_ are retrieved from equation (3.13) and discussed in section 4.1. While, as mentioned before, the income (η _g_ ), price ( ε _g_ ), and cross-price ( ε _gk_ ) elasticities estimates are from Nicita (2004).

The distributional effect illustrated in figure 5.1 suggests that all income groups benefited from trade liberalization. At the same time, the richer the household (to the right in the figure), the more its disposable income increased. While poorest households have gained about 2 percent from trade liberalization, for the richest households the gains are more substantial, about 6 percent. Note that according to equation (5.2), the household’s utility ultimately depends on the level of expenditure needed to purchase a determinate basket of goods, on the value of its agricultural output, and on the wage of its labor. Hence figure 5.2 explores how each group of households fare with respect to these components.

Figures 5.2a and 5.2b illustrate respectively the overall consumption and income distributional effects. Figure 5.2c further divides the effect of change in welfare due to consumption into changes in manufacturing and agricultural prices. Finally, figure 5.2d illustrates the extent to which the change in income is due to changes in the value of the agricultural production and changes in the remuneration of the wages received by household members. From these figures it appears that the only way in which poor households gain from trade liberalization is from the fact that goods in the consumption baskets, especially manufacturing products, are less expensive. Examining the income side of the welfare equation, the results show that the poorest households lose both in 27
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**Table 9: Determinants of the Probability (Probit) of Receiving Vaso de Leche Transfers,** ENNIV 1997

||_Mode_|_lI _||_Mode_|_l2 _||_Mode_|_l3_|
|---|---|---|---|---|---|---|---|---|
||_Marginal_<br>_effect_|_z-stat_||_Marginal_<br>_effect_|_'z-stat_||_Marginal_<br>_effect_|_z-stat_|
|No.Kids0-6|0.117|11.88|**|0.111|11.34|**|0.110|10.85**|
|No.ofkids7-15|0.031|4.14|<br>**|0.025|3.29|<br>**|0.024|3.05**|
|No.ofwomen16-25|-0.008|-0.67||-0.006|-0.53||-0.008|-0.63|
|No.ofwomen26-65|-0.015|-1.04||-0.013|-0.91||-0.017|-1.19|
|No.ofmen_16-25_|-0.021|-1.84|<br>+|-0.024|-2.09|<br>*|-0.026|-2.29*|
|No.ofmen26-65|-0.030|-1.96|<br>*|-0.028|-1.87|+|-0.031|-2.07*|
|Spanishspeakinghead|-0.106|-4.37|<br>**|-0.104|-4.19|**|-0.102|-4.12**|
|Malehead|-0.049|-2.23|*|-0.050|-2.25|*|-0.049|-2.20*|
|Ageofhead|-0.001|-0.30||-0.001|-0.32||-0.001|-0.14|
|Ageofheadsquared|0.0000|0.00||0.0000|<br>-0.02||0.0000|-0.19|
|Logpercapitaconsumption||||-0.177|-10.00|**|||
|Logpercapitaconsumption(IV)|||||||-0.178|-9.51<br>**|
|2nd consumptionquintile|-0.003|-0.16|||||||
|3rdconsumptionquintile|-0.079|-3.08|**||||||
|4thconsumptionquintile|-0.137|-5.77|**||||||
|5th consumptionquintile|-0.240|-8.28|**||||||
|<Departmentdummiesomitted>|||||||||
|No.ofobservations|3,752|||3,752|||3,752||
|Waldchi2|970.52|||973.90|||961.17||
|DF|45|||42|||42||
|Pseudo-R2|0.215|||0.216|||0.213||

_Note:_ Instruments in IV models are education of head, employmnent status of head, dummy for own business/farm, head holds two jobs; jointly significant at I level (F(9,3752)=12.62). ** indicates significance at 99 level of confidence, * at 95 level of confidence, and + at 90 level of confidence.

Poorer households are more likely to benefit from VL transfers. Households in the poorest quintile are 25 percent more likely to participate than are those in the richest quintile and 14 percent more likely compared to the fourth quintile. This difference is additional to any difference due to the greater number of children and other observed characteristics that correlate with poverty such as ethnicity and the gender of the household head. Using Spanishspeaking household heads as a proxy for nonindigenous households, we find no discrimination against indigenous households; indeed they are 10 percent more likely to receive VL transfers than nonindigenous households with similar expenditures. Similarly, female-headed households are _5_ percent more likely to participate.

We now turn to an assessment of how the determinants of VL participation affect the amount of the actual transfers. We pursue this because of the possibility that the determinants of participation and the determinants of the transfer values conditional on participating may _20_
(often recently developed) private sectors.” Finally, we control for a simple time trend, defined by the year in which the Enterprise Survey was completed.

# **4. Basic Results**

Table 1 reports results from probit regressions for BID. Marginal effects are reported instead of unadjusted probit regression coefficients, which are not readily interpretable. Standard errors in all regressions are adjusted for non-independence of errors within each country cluster of observations.

Equation 1.1 tests the OVERALL score (PEFA question 19). Its coefficient is positive and significant, with each 1-unit improvement on the 7-point scale associated with an increase of .01 in the likelihood of bidding for a contract, e.g. from the mean of .188 to .198 (or from 18.8% to 19.8%). Thus, firms located in countries scoring more highly on PEFA question 19 overall are more likely (as hypothesized) to bid on contracts. However, this finding does not tell us which aspects of procurement rules and practices matter more for procurement outcomes experienced by firms. Accordingly, in equations 1.2-1.5 respectively we test each of the four components or sub-indicators of PEFA 19. Coefficients are positive and significant for two of the three _de facto_ sub-indicators, METHODS and ACCESS, but not for COMPLAINT, or for the mostly _de jure_ sub-indicator, LEGAL. The subindicators can take on only one of four possible values (D=1, C=3, B=5, and A=7), so the METHODS coefficient, for example, implies that a 1-grade increment is associated with an increase in BID of 1.8 percentage points. The positive coefficient for ACCESS is consistent with other studies (e.g. Kenny and Crisman, 2016), using entirely different data and methods, that find easier access to information on public procurement increases bidding. Coefficients for AUDIT are positive but not significant in each of the BID regressions.

Larger and older firms, and those with more experienced managers, international certification, a higher share of sales in country, a higher percentage of state ownership, and that are merely one “establishment” in a larger firm, are significantly more likely to bid for contracts. Foreign ownership is associated with a significantly lower likelihood of bidding, relative to the omitted category of private domestic ownership, perhaps reflecting explicit preferences for domestic suppliers. Bidding probabilities increase with the share of state ownership.

15