rafmacalaba/data-use-mentions-extended huggingface dataset:
and means of household expenditures declined, substantial proportions remained.

However, existing literature lacks an assessment of how well reweighting techniques can reduce the biases of poverty projections produced by SWIFT or any other poverty projection method. Using phone or web surveys to estimate poverty necessitates the use of poverty projection methods. Dreze and Somanchi (2023) used actual consumption and income data and showed that a large bias in the poverty rate and mean household expenditure remains even after reweighting but did not assess if reweighting combined with poverty projection methods is 3
schooling _r_ is equal to 7.8%, following the meta-analysis for Central and Eastern European countries (Wincenciak et al., 2022). Using this value and accounting for the 26 weeks of school closures, the value of the adjustment factor to account for the period in which schools were closed _α_ is equal to 0.52. According to the CSO data, the mean annual earnings Y in 2021 are PLN 72,000. Following Psacharopoulos et al. (2021), the present value is calculated over a student's working life period of 45 years, assuming a 3% discount rate. The number of students _S_ equals 6 million (UNESCO, 2022). Finally, the efficiency of distance learning _β_ was set at 0.6 following the Polish school closures duration (UNESCO, 2022) and arguments of Kuhfeld et al. (2020) and Psacharopoulos et al. (2021). The yearly economic loss at the individual level equals PLN 2,927. The present value for the 45 years of working life amounts to a loss of PLN 74,693 for each student. It sums up to PLN 179 billion for the affected population, which equals 7.2% of the 2021 GDP.

# **Conclusions**

The Polish success story of rapid social and economic progress relied strongly on human capital improvement. Unfortunately, this factor is now under significant distress. Significant learning losses have been experienced by Polish students due to the COVID-19-induced school closures. In mathematics and science, the learning losses are equal to more than a year's worth of schooling, even though schools were closed for only part of an academic year. In addition, we show that the 2016 reforms also had a negative impact on student learning. These skills losses are likely to affect the future economic success of the students as well as the country as a whole. Future earnings are projected to decline by PLN 74,693 (more than US$15,000) per year for the affected
POLICY RESEARCH WORKING PAPER 1920

# Summary findings

The transition from a centrally planned to a market economy is likely to have a strong impact on the labor market, on relative earnings, and on returns to education.

Major economic reforms in Vietnam since 1986 (the policy known as _Doi Moi)_ have included a number of measures to liberalize the labor market. It is too soon to asess the full impact of these reforms, but Moock, Patrinos, and Venkataraman analyze the returns to education, on the basis of earnings in 1992-93 (collected in the first Vietnam Living Standards Survey).

This represents one of the first countrywide analyses of the monetary benefits of schooling in Vietnam at a time when the labor market was in transition.

On average, the estimated rates of returns are still relatively low, which is to be expected, since salary reforms were not introduced until 1993. Average private rates of return to primary education (13 percent) and university education (11 percent) are higher than those to secondary and vocational education (only 4 to S percent). Returns to higher education are slightly higher for women (12 percent) than for men (10 percent).

Evidence from other transition economies suggests that returns are likely to increase as reforms in the labor market take full effect. The results support this hypothesis: Returns for younger Vietnamese workers (14 percent) are considerably higher than for older workers (only 4 percent).

Implications for policymaking:

* It is important to monitor future earnings and trends in the labor market, as updates of this analysis could provide more robust estimates of the transition's effects on earnings and returns to education.

* At a time when the Vietnamese government is reassessing its pricing policy, the fact that private rates of return to higher education are relatively high suggests the potential for greater cost recovery.

* Efforts to improve efficiency in secondary and higher education could increase the rate of return by lowering costs.

- This paper a joint product of the East Asia and Pacific,
Table A.5: Sample sizes for different rounds and instruments

|Variable|2012<br>Baseline|2013<br>Midline|2014<br>Endline|
|---|---|---|---|
|No of Schools (principal data)|416|403|390|
|No of Schools (teacher data)|406|387|360|
|No of Schools (student data)|408|417|391|
|No of Schools (inspection data)||387|388|
|No of Teachers (teacher data)|1,165|1,068|1,120|
|No of Teachers (inspection data)||1,057|1,179|
|No of Grade 2 Students|19,621|12,327|—|
|No of Grade 3 Students|18,663|22,053|21,368|
|No of Grade 4 Students|—|14,139|14,765|

Table A.6: Teacher characteristics

|Variable|N|(1)<br>Male<br>Mean/SE|N|(2)<br>Female<br>Mean/SE|N|(3)<br>Total<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|---|---|
|Class size|377|43.363<br>(0.900)|755|52.375<br>(0.558)|1132|49.374<br>(0.494)|-9.011***|
|Permanent|386|0.902<br>(0.015)|784|0.955
For example, in the Upazilas with high local rainfall variability, members of the households with access to credit or safety net are more likely to be self employed in agriculture as compared with households in Upazilas with high local rainfall variability and no access to credit or safety net (Table 3, columns 16 and 17). However, the coefficients of the interaction between local rainfall variability and access to credit and safety net are small as compared with the coefficients of local rainfall variability by itself. Thus, access to credit and safety net is unable to completely negate the need diversify from self employed agriculture to other economic activities when households are faced with higher local rainfall variability.

As with the flood risk, access to market may completely obliterate the households’ need to diversify from self employment in agriculture if they live in Upazilas with high local rainfall variability (Table 3, column 18). Thus we find that access to market may prevent households to be pushed to occupational and employment diversification while access to credit or safety net does not completely prevent such push from flood and local rainfall variability.

These results are not robust to the choices of different occupational focus indicators used in the left hand side of the equations. In some of these cases the interaction coefficients have the unexpected negative sign. In others we do not get the differentiated results between access to credit or safety net in one hand and access to market in the other. These results suggest access to credit or safety net may have similar effect on sectoral or employment focus of households as access to market. These results are presented in Table 3, columns 4 through 15.

**Table 3: Interaction between flood or local rainfall variability and policy action variables.**

||I<br>|nteraction with Floo<br>|dprone Upazilas<br>||||
|---|---|---|---|---|---|---|
||(1)
# **5.2 Robustness checks**

I implement the nearest-neighbor matching estimator suggested by Abadie et al. (2004) for the exposed and non-exposed comparison.<sup>77</sup> The results in Panel A of Figure 2 confirm the positive and significant effect on women’s involvement in decision-making, with an estimate of 0.24 SD. This is a third smaller than the estimate I obtain using PDS Lasso, of 0.36 SD. I also find additional evidence for the reduction in spending on men’s assignable expenditures, with a highly significant coefficient of -1.17 percentage points that is very similar to the PDS Lasso estimate of -1.06.<sup>78</sup> To investigate whether the results could be driven by operators sorting to the lines of the new female supervisors, I estimate the effects on the main outcomes for the subsample of operators who joined the lines exposed to the new female supervisors before the start of the promotion program, comparing them to the previous sample of non-exposed operators. I use the data from the factory follow-up for Uckat and Woodruff (2020) to determine this condition. Panel B of Figure 2 shows that the results are not due to operator sorting. The impact on the decision-making index of 0.29 SD for this subsample is highly significant and similar to the result for the whole sample of exposed operators of 0.36 SD. The impact on men’s expenditures is again negative, though smaller and insignificant at -0.35 percentage points.<sup>79</sup> The Online Appendix shows several additional robustness checks.<sup>80</sup> The large positive impact on women’s decision-making and the negative impact on men’s expenditures are robust to different OLS specifications, a larger set of potential control variables for the PDS Lasso algorithm and a inverse hyperbolic sine transformation. The negative effect on men’s expenditures is also found when using the husbands’ data
section IV, we set the structural price of oil to US$50/barrel, which is the estimated unconditional mean of oil prices from 1960 to 2020. The actual path of commodity prices can be set to any value. Again, in section IV, we analyze the consequences of an increase in oil prices from US$50 to US$80/barrel. The default data source for commodity prices is the World Bank’s Commodities Prices Dataset (WB-CPD– _The Pink Sheet_ ), although other sources are available (e.g., USGS and BP-Energy). Also, the structural production of resources is usually assumed to follow N-year moving average of actual production.

Discoveries of natural resources are calibrated using data on annual production and reserves of the resource good , taken from the BP-Energy (for energy industries) and the USGS (for mining). The time series of discoveries of good in ii period is computed as the change in reserves from period to plus ii production in period (as in equation (3)). In the baseline, the trajectory of tt tt−1 tt discoveries of good from 2020 to 2050 can be set to match the historical average tt over the past 20 years. Naturally, predicting future discoveries of natural resources ii is no trivial task and using historical averages can be misleading. In this case, country-specific data based on experts’ knowledge should be used when available.

The LTGM-NR requires paths for future TFP growth in each sector and industry. TFP data at the industry level is usually unavailable for most developing countries. A simple approach is to assume that TFP growth is homogenous across sectors. In this case, we can set TFP growth in each sector/industry equal to the average aggregate TFP growth over 2000-2019, from PWT 10. When available, the user should use information on sectoral TFP growth.<sup>35</sup> In the Default submodel, private- and public-sector investment data are taken from IMF-FAD, which decomposes total investment into private
[0.003]|[0.003]|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Each year uses information from all available quarters: 2000 uses quarters 2-4, 2007 uses quarters 1-4, 2014 uses quarters 1-2. Sample restricted to individuals working full-time (at least 30 weekly hours of work) age 23-65 and college education. Log hourly wage in constant Mexican Pesos of the first quarter of 2014. Standard errors in brackets.

What does the evolution of wages and wage inequality by cohort and age group tell us? Figure 4 shows that the wages of college-educated workers declined for all ages but they declined faster for the oldest group (51+) and slower for the youngest one (23-30). This is more evident for males than for females. If the degraded tertiary hypothesis is the only channel to explain changes in wages, then we should expect a decline in wages for younger workers. However, as Figure 4 shows, this did not happen for Mexican workers. This is another piece of evidence against the degraded tertiary hypothesis.<sup>7</sup> The observed pattern would be more consistent with the skillsobsolescence explanation. It could be that both hypothesis are playing a role in determining wages, but insofar as these results show, the degraded tertiary hypothesis has a secondary role in explaining large wage changes over the period for college educated workers.

> 7 Figure A3 shows the wage returns by age. The figure illustrates how the wage declines are larger for older workers.

6
Policy Research Working Paper 8878

# **Abstract**

This paper applies an innovative method to estimate poverty in India in the absence of recent expenditure data. The method utilizes expenditure data from 2004-05, 2009–10, and 2011–12 to impute household expenditure into a survey of durable goods expenditure conducted in 201415. At the $1.90 per day international poverty line, the preferred model predicts a 2014–15 head- count poverty rate of 10 percent in urban areas and 16.4 percent in rural areas, implying a poverty rate of 14.6 percent nationally. The implied poverty elasticity with respect to growth in per capita Gross Domestic Product (GDP) is within the range of past experience, and states with higher gross domestic product growth saw greater predicted poverty reductions. In validation tests, the model’s predictions perform comparably to the World Bank’s current adjustment method when predicting for 2011–12 but they are far more accurate when predicting for 2004–05. Three alternative specifications give moderately higher estimates of poverty. The results indicate that survey-to-survey imputation, when feasible, is a preferable alternative to the current method used to adjust survey-based poverty estimates to later years.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@worldbank.org.

_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.
the intersectoral productivity gap fell in the five poorest regions. Among the five richest regions, the intersectoral productivity gap increased in Central and Greater Accra but declined somewhat in Ashanti, Eastern, and Western. Overall, the ratio of intersectoral productivity gaps in industry between the five richest and the five poorest regions roughly doubled from 0.61 in 2003 to 1.3 in 2014.

How closely does this change in the distribution of regional intersectoral productivity gaps relate to recent trends in structural transformation in the country? Between 2003 and 2014, the industrial share of employment and value-added per worker increased, which coincided with the major discovery of oilfields in 2010 (World Bank 2021). Consequently, Ghana’s exports of fuels increased nearly 3.5 times in trade value from 2010 to 2019 (World Bank 2021). More than 76 percent of industrial employment in 2014 is in the four richest regions of Ashanti, Central, Greater Accra, and Western (Figure A3). Given this, the growing importance of extractive sectors is likely to be correlated with the increasing wedge in the intersectoral productivity gap in industry between the five richest and five poorest regions (Figure 13).

# **VII. Conclusion**

The movement of workers from the farm sector to a more productive nonfarm sector has failed to generate a significant gain in labor productivity in recent decades in many developing countries, including in Ghana. We offer a new perspective on the barriers to growth-enhancing structural transformation, by combining structural modeling with recent enterprise census data from Ghana. Specifically, we argue that differences across Ghana’s regions in terms of the productivity gaps between the nonfarm informal and formal sectors impair national productivity gains from structural transformation in the country.

We interpret the disparity in regional intersectoral productivity gaps as reflecting the disparity in regional productivity losses in the intersectoral misallocation of labor, and identify intersectoral misallocation as the output wedge between the informal and formal sectors. Through a series of simulations, we show that a more productive nonfarm informal sector reduces the disparity
# **_Household fixed effect models_**

The base econometric specification does not fully exploit the panel nature of these data.

Accordingly, Equation (1) is re‐estimated, replacing the time‐invariant variables (e.g. distances to markets and banks) with household fixed effects: where D��� is a vector of _time‐varying_ household characteristics and F�� is the price index observed for the community at time _t_ . The term μ� represent household fixed effects.

Table 5 presents the results when specification (2) is estimated. The coefficient on the price index is nearly identical what was obtained in Table 4 and thus not sensitive to the inclusion of household fixed effects.

# **_Food payments as commitment devices_**

Some households might prefer food payments rather than cash because – absent the sale of the transfer – food pre‐commits the households’ consumption of the transfer.<sup>16</sup> Although the data in the 2006‐2014 household panel survey do not allow this to be tested formally, the 16 Speaking about the debate regarding replacing India’s food transfer system (the Public Distribution System, PDS) with cash payments, Dreze notes that one of the concerns raised by PDS participants is that “They are worried that cash can be more easily misused than food, because food can only be consumed in small quantities over time while cash is easily spent in one go”. http://www.ideasforindia.in/topics/miscellany/ashok‐kotwal‐speaks‐with‐ jean‐dreze.html. Accessed July 24, 2018.

21
<br>(1.45)|-0.59<br>(1.31)|
|6-10 employees|0.84**|0.47|
||(0.4)|(0.31)|
|11+employees|0.62**<br>(0.25)|0.2<br>(0.17)|
|Missing enterprise size|1.84<br>(1.8)|0.26<br>(1.73)|
|Constant|-1.66*|0.44|
||(0.97)|(0.38)|

_Source_ : Authors’ analysis based on survey data. _Note_ : Class 3 is the omitted class. Omitted education level is none; omitted enterprise size is 1-5 employees. Sampling weights are not applied. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels, respectively.

50
721 households in six provinces, CBT and PMT would add an additional estimated $276,586 and $502,884, respectively, corresponding to 2.18% and 3.97% of the total program budget.

**Data access and privacy** Access to phone data is necessary for CDR-based targeting. As we show, targeting performance degrades considerably when CDR are not available for subsets of the population. Encouragingly, the past several years have been characterized by a trend towards public sector access to CDR, particularly in the context of the COVID-19 pandemic, during which mobile network operators shared CDR with governments, researchers, 12In our cost calculations we obtain estimates for a CBT, rather than the hybrid approach used in the TUP program, as there is more information available on CBT-only costs in the literature. However, as the CBT cost can be interpreted as a lower bound for the cost of a hybrid approach, our qualitative results also apply to a hybrid approach.

> 13Marginal costs of CDR-based targeting are negligible because we assume no contact with screened individuals is required. In practice, it may be desirable to solicit informed consent to access CDR. If consent were collected in-person, the marginal costs would approach that of a PMT; if collected over the phone, there would still be significant cost savings, see Appendix Section C).

16
A wide number of factors contribute to the performance of economic zones programs, including structural determinants like market size and access and broad national competitiveness (see Farole, 2011). But one of the main factors contributing to unsuccessful zones may be the failure to create an _investment climate_ inside the zones that is substantially improved on what is available outside. Anecdotal information suggests that too often, African zones are plagued with the same problems – unstable electricity, lack of water, heavy bureaucracy, inefficient and corrupt customs – that hinder investment in the wider economy. However, to date there have been no comparative studies of the investment climate performance in economic zones, in Africa or elsewhere.

This paper has two objectives: first, to assess the outcomes of African economic zones in a systematic manner; second to assess the performance of African zones in meeting the investment climate requirements of their investors. The hypothesis is that the latter has important impacts on the former. The paper is based on original survey research carried out in 6 African countries (and four comparator countries outside the region). Throughout the remainder of the paper we will refer to the zones programs as being “export processing zones” (EPZs), even though several of the programs are technically considered free zone programs – the reason for this is that the focus of the research was on export‐oriented manufacturing activities and thus in line with the traditional EPZ model.

We find that performance across the African zones is mixed, with Ghana and Lesotho (and in some cases Kenya) performing relatively well on some measures. On the whole, however, African programs are underperforming relative to the Asian and Latin American programs included in the study. Moreover, the African zones programs are not yet having any discernable structural impacts on their economies. The results raise questions regarding the competitiveness of African zones programs and the potential of zones which aim to compete for labor intensive assembly employment under a traditional export platform (EPZ) model. In analyzing investment climate performance, we find that,
.1|0.1|0.3|0.0|
|**Factor Earnings**||||||||||
|Skilled labor|1.0|0.4|0.0|0.3|0.0|0.1|-0.1|0.3|0.0|
|Unskilled labor|2.6|0.3|0.0|0.1|0.4|-0.3|1.2|-0.1|0.5|
|Capital|2.0|0.5|0.0|0.2|0.2|0.3|0.2|0.2|0.3|
|**Diagnostics****||||||||||
|Feenstra Ratio Summary|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|
|Terms of trade|100.36%|99.98%|100.00%|99.66%|100.37%|99.60%|100.42%|99.73%|100.56%|

***** We impose an upper bound of 90 on the elasticity of substitution between FDI and domestic business services (30 times the Krugman value of σ=3); in this case, we fall short of fully matching the FDI response in the Krugman model. **The percentage change is the value indicated minus 100.

Source: Authors’ estimates 54