rafmacalaba/datause-encoder-data huggingface dataset:
Japan and the Asian Tiger countries, the share of employment in agriculture was near negligible and the dominance of manufacturing had been replaced by service industries.

In China and India, a reallocation of labor from agriculture to manufacturing is ongoing. The crux of the impressive decline in Chinese poverty rates lies in the absorption of large pools of relatively unskilled agricultural workers first in light and then more sophisticated manufacturing industries. In India, the pick-up in manufacturing employment has been slower than in China. Thus far, agricultural labor has been absorbed more by services than manufacturing industries (Figure 1.5).

At least two characteristics make the structural transformation of the Chinese and Indian economies remarkable. First, it is unfolding in a fiercely competitive globalized world where the competitive edge of firms in manufacturing and services is dependent upon wage levels, and the firms‘ ability to exploit the latecomer advantage by adapting modern technologies innovated by the developed countries. And second, given that the proportion of the labor force engaged in agriculture was as high as 80 percent until the 1960s, the speed of their transformation from agrarian to modern economies is significantly more impressive than richer countries. Both characteristics provide useful pointers for other low-income countries.

**Figure 1.5: Structural transformation in successful developing countries:** **<u>reallocation of labor from agriculture to manufacturing</u>**

<!-- Start of picture text --> 35%<br>90%<br>80% 30%<br>70%<br>25%<br>60%<br>50% 20%<br>40%<br>30% 15%<br>20% 10%<br>10%<br>0% 5%<br>0%<br>1870 1890 1910 1930 1950 1970 1990<br>Japan India South Korea India South Korea Singapore<br>Singapore Taiwan, China China Taiwan, China China Japan<br>employment (%)<br>AShare of agricultural employment in total Share of Manufacturing employment in total employment<br><!-- End of picture text --> Note: After 2002. The employment statistics for China is not comparable with earlier years because the NBS stopped publishing data for rural employment. Source: ILO,UNIDO and UNCTAD In general, the factors that powered structural transformation in the Western, East Asian and emerging market countries have been elusive in Sub-Saharan Africa (excluding South Africa), and suggest that there are no shortcuts to catching up (Figure 1.6). As an example, with the 9
respect for their husbands and encourage continued support from them. Men benefit by retaining their status as the household’s ultimate decision-maker. This financial management arrangement means that women are often relatively excluded from large asset management and ownership, but are responsible for daily care and sustenance for the family (Guerin, 2006; Garikipati, 2008; Garikipati et al., 2017).

While acknowledging women’s subordination, as well as common patterns in resource management practices, more recent scholarship has noted that intrahousehold relationships are not fixed according to a static contract, but rather are dynamic and constantly renegotiated (Jackson, 2007; Jackson, 2012; Johnson, 2017). Economic exchanges play an important role in the definition and evolution of those relationships (Zelizer, 1994, 2012). Despite their relative disadvantage, women exercise agency within ongoing bargaining over the management of household economic resources (Kandiyoti, 1988). Crucially, couples bargain not only about how to spend the money they have, but also who should be responsible for meeting a variety of household needs (Guyer, 1988). In other words, household members bargain over who _should be expected to pay_ for each type of expense on a regular basis. Contemporary urban households in Sub-Saharan Africa experience a high degree of instability in income because of the dominance of the informal economy, in addition to facing shifting needs over the life course (Collins et al., 2009). Because economic conditions are unstable, opportunities to reshape resource management practices are frequent.

Existing studies of intrahousehold resource management have provided rich descriptions of common practices and dimensions of variation (Johnson, 2017), but there has been relatively little work linking those practices with the entrepreneurial behaviors of household members. Johnson (2004) examines how women’s income and expenditure flows affect their demand for 5
## SPLIT DECISIONS

near the cutoff, suggesting a regression discontinuity design to evaluate the effects of migration on EPS job-applicants’ households. Migration by test-passers typically occurred within a few months of the KLT, and our household survey occurred in early 2010. Households are therefore surveyed about 3–5 years after potential migration began.

# 4.2. The regression discontinuity design

We estimate the effects of migration with the regression discontinuity design or RDD. Because RDD results can be sensitive to functional form assumptions, we use fully nonparametric sharp and fuzzy RDD (Hahn et al. 2001; Porter 2003; Nichols 2007; Imbens and Lemieux 2008). Because RDD results are also notoriously sensitive to bandwidth selection, we tie our hands with the asymptotically optimal bandwidth recently proposed by Imbens and Kalyanaraman (2012).<sup>14</sup> For each outcome _μ_ we first estimate the intent-to-treat (ITT) effect (where treatment _τ_ is migration) with sharp nonparametric RDD. This is the effect of barely passing the language test on “compliers” (Angrist et al. 1996)—those whose migration decisions were changed by the test result—whose families were willing to complete the survey, and who had scores near the passing threshold _s_ = 0:<sup>15</sup> We then estimate the treatment-on-treated (TOT) effect as fuzzy nonparametric RDD. This is the effect of migration by a household member: This again is the local average treatment effect on “compliers”, whose households com-

> 14We use the triangular kernel shown optimal by Fan and Gijbels (1996) and Cheng et al. (1997) for its boundary properties; Lee and Lemieux (2010) argue that the choice of kernel “typically has little impact in practice.”

> 15For individual _i_ the outcome is _μi_ , treatment status is _τ i ∈{_ 0 _,_ 1 _}_ where treatment is migration, and _s ≡_ [raw score] _−_ 120. Then _μs↓_ 0 _≡_ lim _s→_ 0+ _Ei_ [ _μ_<sup>_i_</sup> ]; _μs↑_ 0 _≡_ lim _s→_ 0 _− Ei_ [ _μ_<sup>_i_</sup> ]; _τs↓_ 0 _≡_ lim _s→_ 0+ _Ei_ [ _τ_<sup>_i_</sup> ]; _τs↑_ 0 _≡_ lim _s→_ 0 _− Ei_ [ _τ_<sup>_i_</sup> ].

11
<u>Table 5 : Housing Subsidies in Morocco – Detailed List and Amounts as of 1995</u>

|<br>_Category_|<br>_Provision_|_Subsidy_<br>_Amount_<br>_(million_<br>_MDH)_|
|---|---|---|
|**Fiscal Expenditures**|||
|Registration Fees|Reduced rate : 2.5% instead of 5% for dwellings with<br>value under Dh 350,000<br>Reduced rate : 1.25% instead of 5% for dwellings with<br>value under Dh 180,000|(total)<br>458*|
|VAT|Reduced VAT rate for housing sector (14% instead of<br>20%)<br>No VAT on self-construction|824<br>222|
||No VAT on social housing (area < 100 m2 and value ,<br>200,000 DH)|44|
||ExemptionofVATon interestsfor mortgages|63|
|IncomeTax|Deductionof mortgageinterestsfrom taxableincome|276*|
||Deductionof 40 %from rental income|n.a.|
||Deduction of mortgage interests and capital from taxable<br>income, for wage earners buying social dwellings (Value<br>< MDH500,000)|n.a.|
|Tax on Capital Gains|Total exemption after 10 years possession, partial<br>exemption after 5 years possession<br>Exemption of the first sale of social unit (area < 100 m2<br>and value ,200,000DH)|570*<br>n.a.|
|Taxe Urbaine|Tax base cut by 75 % for homeowners<br>Exemption for new unitsfor thefirst5 years|275<br>100|
||Exemption for social housing for all the duration of the<br>loan|5|
|Taxe d’édilité|Taxbase cutby 75 %for homeowners|992|
|Construction Tax|Exemption for social units built on parcels with area <100<br>m2|13|
|**Total**||**3842**|
|**Direct Subsidies**|||
|Housing<br>Ministry<br>Investment Budget|Investment<br>expenditures<br>for<br>slum<br>upgrading<br>and<br>infrastructure servicing of theinformalsectorprograms|144|
|Interest rate subsidies on<br>mortgages|Applies to households buying dwellings from the Housing<br>Bank(CIH)for lessthan MDH. 500,000|181|
|Exchange rate risks|Treasury covers the exchange rate risk on loans serving to<br>finance social housing programs|20|
|**Total**||**345**|
|**Land subsidies**|Provision of land parcels from the State domain to public<br>developers for construction of social housing, at prices<br>belowmarket|**637**|
|**Grand Total**||**4824**|

Source : Etude PLURAM INTERNATIONAL-ALGOE-PROMOCONSULT, Ministry of Housing, Morocco (1999), except for items with a * : Author’s calculations from World Bank (1995). n.a. : not available.

30
demand eight years after hyperinflation, its result would be to _reduce_ demand relative to pre-2009, just as the effect is that those taking up sector activities are _more_ likely to operate out of need.

The data include a measure of the years of experience of the largest owner in the specific “industry of” activity—that is, for example, in making handicrafts or baking bread. This is a different measure than from the age of the business, or for that matter, from the age of the owner. In fact, the average informal business has been in operation for 4.9 years, while the average owner has been active in that area of activity for 7 years.<sup>38</sup> This is suggestive that informal business owners took up their activity outside of a business context or possibly had a previous operation. A concern would be if the measure of industry experience is simply reflecting age, savvy, or longevity in the informal sector, all of which could plausibly be related to bribe exposure (through for example knowledge that paying a bribe has certain known payoffs for continuing to operate or to use savvy to avoid corrupt officials). In this case, it is important to control for age of the owner. For the instrument, a dummy variable is created, taking a value of 1 if the owner started in that “sector” after 2009, 0 otherwise. If this variable is sufficiently, strongly correlated with the measure of opportunity cost _and_ sufficiently exogenous to bribe exposure, it suggests a valid instrument; by contrast this measure would be vulnerable to further endogeneity if there are other unobservables correlated with both the industry experience as well as bribe exposure.

# _-_ _<u>Results of Two Stage Estimations</u>_

Table 5 shows the estimated effects (LATE for 2SLS and ATE for biprobit) from two-stage estimations of the full models from Table 3, Columns 2 and 4; the single variate probit estimates are re-presented here as well, for comparison. Each two-stage model uses only one additional, excluded instrument in addition to all co-variates (which are in both stages). Appendix A.VI includes several diagnostic statistics for both two-stage methods. From the 2SLS results, Olea and Pflueger’s (2013) weak instrument test —which can handle the use of non i.i.d. standard errors, such as the clustered standard errors required by the use of complex survey data—strongly rejects the null of a weak instrument with one endogenous regressor.<sup>39</sup> However, only the LM Kleibergen-Paap rank statistic for NOWAGE suggests at any level of confidence (and even then weakly with a p-value=0.099) that

> 38 Survey-weighted; age of business 25th to 75th percentile is 1 to 6 years. Years in “industry” 25th to 75th range is 2 to 10 years. The two variables have a correlation of 76.7% (weighted), significant at 99% confidence.

> 39 Implemented using the user-written command weakivtest in Stata, using a tau, threshold value of 5% with 95% confidence.

(26)
female employment or total employment. (v) Finally, we exclude countries with fewer than 100 manufacturing firms after applying the previous steps.

The procedure above results in more than 29,000 manufacturing firms in 64 countries. The list of countries, year of the most recent survey and number of firms by country can be found in Appendix 1. The distribution of firms across ISIC sectors is shown in Appendix 2.

We combine the firm‐level data with Global Employment Sex‐disaggregated Statistics (GESS) at the sector level from the World Bank Household Surveys and other public resources. GESS uses harmonized classifications of economic activities and occupation categories. It fills an important information gap in global sex statistics by providing more detailed accounts on education, employment levels, wages, labor income, and employment status at very disaggregated economic activity levels and occupation categories than is usually available. The data are available for 50 countries at the ISIC Rev. 3.1 level, but only 30 countries overlap with the Enterprise Survey data sample. These 30 countries are highlighted bold in the country list of Appendix 1.

# **2.3 Measures**

This study focuses on two measures of _female labor participation_ :

- Female labor share, _fem_sh_ = number of permanent full‐time female employees as % of total number of permanent full‐time employees. The variable is computed for permanent production workers, non‐production workers and total workers (sum of both). Non‐production workers include managers, administration and sales.

- Female wage share, _S_<sup>_F_</sup> = total compensation of female employees as % of total compensation of all employees. Since the Enterprise Survey dataset has only information on average wage rates,<sup>10</sup> we approximate female wage rates at the firm level by assuming the same ratio of female‐to‐ average wages in a given sector as in the gender‐disaggregated wage data from the World Bank Household Surveys. Total female compensation is then obtained by multiplying the female wage rate with the number of female employees from the Enterprise Surveys.

The analysis examines two measures of _trade participation:_

> 10 These are obtained dividing a firm‘s total compensation by its total number of employees.

7
# **_Impacts on Local Prices_**

The second step in the proposed causal chain from a local cash influx to nutrition-related impacts on nonbeneficiaries, after a rise in local aggregate demand, is the presence of higher food prices. We thus explore the covariation between the rollout of _Pantawid_ and food prices. We begin by examining changes in the household reported prices of a few individual food goods recorded in the 2011 experimental sample. This information was recorded only for three individual foods of standardized quality – eggs, rice, and sugar.<sup>21</sup> Eggs are therefore the key non-tradable protein in this portion of the analysis, while rice and sugar are storable and hence more easily traded foods.

Figure 2 shows the relationship between height-for-age z-scores of children ages 0-3 years with respect to the price of eggs and that of rice. We see that height-for-age decreases in the price of eggs but not in the price of rice, an indication that the price of protein is a factor in determining child height-for-age. Figure 3 shows how the price of eggs co-varies with program saturation in treated and control villages. First, at low levels of saturation, there is no difference in the price of eggs between treated and control villages. In control villages, the relationship between price and (potential) program saturation is flat or, if anything, slightly negative, perhaps reflecting that higher saturation control villages are poorer (as higher saturation means more households below the provincial poverty threshold.) However, in treatment villages, as saturation increases, and particularly past the median level of 65 percent, we observe a positive relationship between prices and saturation. The price of eggs, a key signal protein-rich food, thus covaries with program saturation.

Panel A in Table 2 explores through a regression framework how price levels vary at the time of the survey between program and control villages. None of the price differences are significantly different from zero and the point estimates for rice and sugar are close to zero as well. Although not precisely estimated, the point estimate for the price of eggs stands almost 2 percentage points higher, indicating some divergence in relative price difference between the storable goods, such as rice and sugar, and the perishable good, eggs.

Price differences emerge more clearly when the program indicator is interacted with saturation. Panel B in Table 2 shows that egg prices increase by 16 percent as we go from zero to

> 21 Eggs refer to chicken eggs, rice to commercially milled rice, and sugar to commercially sold brown or white sugar.

17
7 lead to greater willingness to pursue policies that are less disproportionately beneficial to them and their core supporters. Several variables in this database, ranging from indices of electoral competitiveness, to the vote shares of parties and directly elected executives, permit crosscountry investigation into questions such as these.

Poverty alleviation and voice. The construction of a safety net for the poor, or of programs that allow the poor to build human and financial capital to rise out of poverty, is often distorted by political imperatives. Pritchett and Gelbach (1997), for example, have provided evidence and theory indicating that targeting assistance to the poorest is unsustainable over time. In a simple majority-rule setting, they show that the less poor majority eventually demand more expansive coverage of targeted programs as a condition for their continued support of them. However, the structure of political institutions can mitigate —- or exacerbate - this outcome. The rules governing election to the US Congress, for example, give an incentive to legislators representing the inner city and to those representing farming districts to agree on the continued provision of narrowly targeted food stamps. Such incentives are less strong in countries where legislators represent broader segments of society, most of whom _ gain little or lose from targeted assistance. The DPI contains variables that help to capture some ofthe institutional features that would affect the sustainability of targeting, including the presence of legislatures and rules governing election to legislative chambers. Economic tradeoffs in constitution drafting. When tremendous institutional transformations occur, from totalitarianism to democracy, or even from one constitution to another, they entail numerous detailed decisions from among a broad array of institutional choices. Here, again, the empirical evidence that should inform these choices - e.g., which electoral rules? how should the executive be selected? — has been largely unavailable. As a consequence,
Next, we address the concern that wage differentials merely reflect differences in wage trends across the winning and losing counties and, more importantly, differences which would have prevailed even in the absence of a new plant introduction. To appease such concerns, we investigate wage trend differentials in the winning and losing counties before and after the new plant introduction. To conduct this exercise, we replace the post **1** _t>τ_ +1 dummy with yearly dummies 4 years before and after the deal announcement. The coefficient _β_ 2 _,t_ now varies by year and captures the relative wage differentials between the winning and losing counties for year _t_ relative to the new plant introduction. Figure 3 plots the coefficients _βt_ before and after the MDP creation. The wage pre-trends are insignificantly different from zero for three of the four pre-periods, and the linear trend, if anything, is downward sloping. This trend reverses following a new plant introduction. The estimates are significantly positive in four of the five periods during the post period. More importantly, the linear trend switches to being upward sloping. This switch in wage trends indicates a trend break around an MDP introduction.

# **4.2.2 on Household Time Allocation**

How do households respond to wage shocks induced by new plant introductions? Our theory, as well as that obtained from an alternate setup introduced in Greenstone et al. (2010), suggests that households respond to increasing equilibrium wages by allocating more time to labor and less to other activities like shopping, or leisure. In Table 8, we investigate whether this prediction is validated empirically. The CPS March annual socio-economic supplement asks respondents about the total hours worked in the previous week, as well as the number of hours spent on the job, on average. A validity of our model prediction requires respondents to spend more time on labor, relative to the average time spent, in areas where a new plant is introduced. In other words, we expect _β_ 2 in specification (S3) to be positive and significant. If, on the other hand, a wage increase does not lead to changes in time utilization, the interaction term should not be significantly different from zero. The estimates reported in Table 8 point to a statistically and economically large substitution effect. While the respondents work 35 _._ 14 hours per week on average, an introduction of a new establishment increases weekly labor hours by 11.3%; that is, 3 _._ 9 hours more in winner counties relative to the loser counties. This estimate is robust to inclusions of stronger deal 29
outcomes, which require further evidence for better understanding (Cerra _et al.,_ 2022; Ferreira _et al._ , 2022). In fact, we offer the first study to investigate all three development outcomes of poverty, inequality, and economic growth for Viet Nam.

Specifically, we investigate several research questions that are highly relevant to policy. Have average incomes across provinces converged (or diverged) over time? If yes, how were the poor segregated? Why were the poor spatially concentrated in some provinces and not others? Did factors such as inequality, urbanization, investment, and volume of government spending play a role in determining poverty reduction? Furthermore, what could we tell about the relationship between economic growth, inequality, and (different measures of) poverty?

Second, our analysis spans the period 2002-2020, which represents the longest-running period for the country that has been examined in an academic study. We complete this task by analyzing 10 rounds of the nationally representative Vietnam Household Living Standards Surveys (VHLSS). Our findings on the trends of poverty, inequality and economic growth, which are further disaggregated into between-province and within-province and regional variations, offer a nuanced picture of the evolution of these outcomes over time. To our knowledge, these findings are new and not available in previous studies.<sup>1</sup> Finally, we construct a database of panel data at the province level, which allows us to offer more granular analysis than the urban-rural dichotomy analyzed in previous studies. We further

> 1 Among the three development outcomes of poverty, inequality, and economic growth that we analyze, existing studies focus on either a single outcome or two outcomes. Their analyses are mostly restricted to survey data before 2010. For example, analyzing earlier VHLSS data covering the early 1990s to the early 2000s, Le and Booth (2014) and Nguyen _et al._ (2007) found widening urban-rural gaps in inequality. Benjamin _et al._ (2017) examine household income inequality during 2002-2014 through several dimensions, including urban-rural, ethnicity, and work sectors. Bui and Imai (2019) examine urban-rural gaps in Viet Nam during 2008-2012 and find education and remittances to be important factors. For other studies that focus on the ethnic gaps in living standards, see Dang (2012) and Fujii (2018).

3