rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9919

# **Abstract**

With only 32 percent of working-age women in the labor market, Guatemala is an upper-middle-income country with one of the lowest rates of female labor force participation in the Latin America and the Caribbean region, and in the world. The rate of female labor force participation is especially low in the poor regions of the North and the Northwest. This paper explores information from different micro data sets, including the most recent Population Census (2002 and 2018) to assess the drivers of the recent progress. Between 2002 and 2018, the female labor force participation rate increased 5.7 percentage points, from an average of 26 to 32 percent nationwide. This increase was partly explained by the drastic increases in the educational attainment of women, the reduction in fertility, and the country’s structural transformation toward services. However, a large component remains unexplained. Exploring 2018 data, the paper shows that social norms, attitudes toward women in society, and public policies are important determinants of these changes. The analysis suggests that, taken together, these factors can all become an important source of increased female labor force participation moving forward.

This paper is a product of the Human Development Team and the Central America and the DR Country Management Teams, Latin America and the Caribbean Region. 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 ralmeida@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
|7.0|-12.4|89.9|82.1|-92.8|
|Miscellaneous articles|20.2|26.0|5.8|6.7|-2.9|46.4|
|**Average**|**17.7**|**13.3**|**-4.4**|**66.3**|**53.1**|**-15.4**|

- Source: Authors’ calculations based on UNCTAD TRAINS and Chemingui & Dessus (2008).

- Change between 2002–09 chosen on the basis of available data. Tariffs are unweighted; import-weighted changes average -5.2.

- Change between 1999–01 chosen on the basis of available data. Nontariff barriers are estimated using a price-differential decomposition; see Chemingui & Dessus (2008) for details.

the balance of payments deficit to 6.6% of GDP, increasing nonoil exports by about 13% annually, and increasing private sector exports by 15% per year.

There are reasons to believe that the policy decision to diversify the production and export base was primarily a political decision that—while influenced by historical structural features of the economy—was not directly influenced by contemporaneous economic developments. While the government explicitly acknowledged the structural imbalances across industrial sectors caused by the centralized system of the past, it is also clear that the existing changes in the structure of the economy were hitherto unremarkable and hence did not play a role in its policy choice (State Planning Commission 2005, p. 48):

[The pattern of Syrian] trade has been marked for a long time by a stereotyped nature. . . predominant in most developing economies. . . economic development has not achieved, over the last 50 years, its goals of adjusting the production structure, but. . . increased the dependency on importing local product inputs.

In part
selected several models were trained and evaluated using a cross‐validation approach. The final model was used to predict poverty rates at the 100m*100m level into all settled areas of the country including where survey data were not available. To aggregate the estimates at the state and county level, the 100m*100m level are weighted using a newly developed data set of human settlements across South Sudan constructed by combining a variety of publicly available data sources.

This paper describes the design and analysis of the third wave of the HFS in 2016.<sup>8</sup> The paper is focused on Wave 3 of the HFS, conducted between mid‐2016 and early 2017, representing the most recent wave covering both urban and rural areas. Furthermore, the period between late 2016 and early 2017 was a critical period in South Sudan’s history, when the conflict and refugee crises were reaching their peak. In Section 2, the paper describes the survey design and implementation, including the deviations from the original sample frame presenting consistency‐checks used to evaluate potential selection issues that affect representativeness. Section 3 will detail the process of calculating consumption aggregates and estimating poverty using within‐survey multiple imputations, including calculating durables consumption flow and spatial‐time deflators. Section 4 gives a brief overview of the results of the poverty estimation, while a comprehensive assessment of poverty trends is available elsewhere (World Bank, 2018). Section 5 describes the estimation of poverty rates using satellite data as a proof‐of‐concept while Section 6 concludes the paper with a short discussion of main limitations.

# 2. Survey Design and Implementation

## Sample Design

The 2016 Wave of the HFS was conducted between mid‐2016 to early 2017 and consisted of the second nationally representative survey wave of the HFS. The survey covered rural and urban areas across 7 of the 10 former states of South Sudan. The regions covered include Greater Equatoria, Greater Bahr el Ghazal, and Lakes. The 10 former states are used in planning for
Similar to the energy sector, the transport industry is vulnerable to both climate-related conditions and the risks associated with climate transition – think economywide carbon pricing, climate-smart transport policies, new transportation solutions, among others. As one of the leading contributors to carbon emissions, responsible for 24% of CO2 emissions from fuel combustion on a global scale, the transport sector is expected to undergo a significant transformation (IEA, 2020). These long term and far reaching effects are often overlooked in analysis, but are crucial for shaping effective policies. Section 3.3.1 discusses examples of government response, including carbon tax and energy pricing, and their impact on employment.

# **2.3. Labor supply and time allocation**

Changes in climate may affect labor supply and time use in various ways. First, weather conditions may encourage worker absenteeism. In India, a rise in the 10-day average temperature by 1<sup>_◦_</sup> C WBGT<sup>xxi</sup> increased the probability of absenteeism among steel workers by 2% and among garment workers by 10%, but not for piece rate workers in cloth-weaving factories (Somanathan et al., 2021). Workers in Chinese manufacturing, however, are not more absent on hotter days (Cai et al., 2018).<sup>xxii</sup> Weather conditions may also encourage shirking. The quality of reported data among Demographic and Health Survey (DHS) interviewers declined significantly on hot days, with more missing responses per interview on average, while the total number of interviews conducted per interviewer remained unchanged (LoPalo, 2023).<sup>xxiii</sup> Second, workers may adjust the distribution of work time in response to weather shocks. In one example work time is reallocated from farm to off-farm work. Indian households that experienced a persistent annual decline in farm income due to drying up wells (caused by climate change) are 12 percentage points more likely to derive income
‐being and Prices

_Well-being_ . Among all the papers reviewed, 13 papers explicitly measure the impact of displacement on the economic well-being of host communities for a total of 64 distinct results. Of these, 34 are on income, consumption or output, 20 are on housing or assets, 6 are on night luminosity and 4 are on poverty. Only two of these results are on HICs, 26 are on MICs and 36 on LICs. In all these cases, a positive result is considered a good outcome.<sup>18</sup> Table 7 shows that between 45 and 52% of results are positive and significant depending on whether results are weighted for the journals’ impact factor or not. This indicates a net improvement in household wellbeing according to about half of results. An additional, 34-42% of results are non-significant, and the remaining 6-20% of results are negative and significant. Looking more in details at the 13 negative results, these are equally split between income and consumption and assets and housing indicators. Results on assets and housing indicators refer to individual items such as construction materials of dwellings and are therefore less representative of household well-being as compared to aggregate income or expenditure.

Overall, between 80 and 94% of results are either positive or non-significant indicating that the likelihood of a forced displacement shock resulting in a negative outcome for host communities is less than 20%. The basic theory illustrated at the outset of the paper could not predict these outcomes whereas such outcomes are clearly in contrast with the popular view that forced displacement is detrimental to host communities. These results are encouraging but much more research on household well-being is needed. Future research will need to provide more hard evidence based on income, consumption or expenditure indicators and expand its coverage to many more countries and situations and possibly disaggregating results by type of households.

> 17 These dummies could not be used in the multivariate analysis because of collinearity with other variables.

> 18 Note that when poverty was used as an indicator of well-being, the
shallian argument suggests that the shift towards fixed rent contracts should increase yields. Remittances (which we cannot observe in our data) may further increase yields by facilitating large-scale investments, such as in tractors and irrigation systems. However, high-skilled migration may reduce yields through a loss of labor (as in Rozelle, Taylor and De Brauw (1999)); specifically, a loss of the landlord’s labor inputs and know-how which may not be easily replaceable.

In Appendix Table A13 we present the result of the standard IV specification, with the yield of all major crops (in kg/ha in natural log) as a dependent variables. Due to the large number of connected variables, we apply a standard multiple hypothesis correction following Anderson (2008). Note that the sample size varies as not all crops are cultivated in all districts.

We note imprecisely estimated null effects for the majority of crops. Focusing on the main grains, the food crops, we note a positive impact on rice and maize (estimated at 5% and 4%. respectively), and a negative impact on wheat (estimated at 5%). The former is consistent with Marshallian theory, with the labor incentive (and possibly remittance) effect dominating. The latter implies the dominance of other channels related to the loss of crop-specific knowledge and labor.

# **8 Conclusion**

This paper contributes to a longstanding question in the study of economic development: the causes and consequences of the reallocation of labor from rural to urban areas. Employing an empirical strategy that exploits districtlevel bilateral migration network data, we find that in early 21st century India, out-migration of high-skilled males contributed to the shift from sharecropping to fixed rental arrangements. This is a to-date undocumented consequence of rural-urban migration in developing countries, with important implications for efficiency and welfare.

High-skilled out-migration, through an institutional response of changed rental contracts, appears to increase yields for some key crops, countering the 34
papers/w26983.

- D. Balla-Elliott, Z. B. Cullen, E. L. Glaeser, M. Luca, and C. T. Stanton. Business reopening decisions and demand forecasts during the covid-19 pandemic. Working Paper 27362, National Bureau of Economic Research, June 2020. URL http://www.nber.org/papers/w27362.

- A. Balleer, S. Link, M. Menkhoff, and P. Zorn. Demand or Supply? Price Adjustment during the Covid-19 Pandemic. CESifo Working Paper Series 8394, CESifo, 2020. URL https://ideas.repec. org/p/ces/ceswps/_8394.html.

- J. M. Barrero, N. Bloom, and S. Davis. Covid-19 is also a reallocation shock. _Brookings Papers on Economic Activity_ , Forthcoming(July), 2020.

- A. W. Bartik, M. Bertrand, Z. Cullen, E. L. Glaeser, M. Luca, and C. Stanton. The impact of covid-19 on small business outcomes and expectations. _Proceedings of the National Academy of Sciences_ , 117 (30):17656–17666, 2020.

- I. Bartlett, Robert P and A. Morse. Small business survival capabilities and policy effectiveness: Evidence from oakland. Working Paper 27629, National Bureau of Economic Research, July 2020.

- T. Beck, B. Flynn, and M. Homanen. Covid-19 in emerging markets: firm-survey evidence. Covid Economics, Vetted and Real-Time Papers 38, VoxEU, 2020.

- C. Bircan, R. De Haas, H. Schweiger, and A. Stepanov. Coronavirus credit support: Don’t let liquidity lifelines become a golden noose. Technical report, VOX CEPR Policy Portal, 2020.

- B. Bonadio, Z. Huo, A. A. Levchenko, and N. Pandalai-Nayar. Global supply chains in the pandemic. Working Paper 27224, National Bureau of Economic Research, 2020.

14
Policy Research Working Paper 5890

# **Abstract**

Over the past two decades, the Jordanian education system has made significant advances. Net enrollment in basic education increased from 89 percent in 2000 to 97 percent in 2006. Transition rates to secondary education increased from 63 to 79 percent in the same period. At the same time, Jordan made significant gains on international surveys of student achievement, with a particularly impressive gain of almost 30 points on the science portion of the Third International Mathematics and Science Study. Changes in test scores over time are presented and analyzed using decomposition analysis. The trends are related to policy changes over time. It is argued that benchmarking education systems and constant feedback between researchers and policymakers contributed to this achievement.

This paper is a product of the Education Team, Human Development Network. 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 author may be contacted at hpatrinos@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. 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
by Feder and Akihiko, 1996).6 Distinguishing it from the latter two effects, which result from liberalized transfer rights, faces a number of challenges, including the fact that transfer rights and tenure security often move together. Fortunately, the theory developed in Section 1 below suggests a number of quite specific tests that permit us to distinguish security from regret and factor price equalization effects. Many of these tests are variations on what have become known as separabilty tests that ask whether shadow prices and factor choices in production independent of household endowments and consumption choices. Interestingly, the modeling done here suggest that much conventional separability testing has been misspecified in the sense that non-separability, if it holds at all, applies differently across different market participation regimes and that a single regression model that pools observations across regimes is inappropriate.

Section 2 develops an econometric implementation of the theoretical model. While offering a powerful, if statistically conservative, control for latent variables, the panel data methods put forward force reliance upon simulated maximum likelihood (SML) methods given multiple land market participation regimes and the presence of censoring in the investment data. Using data collected in 1988 and 1993 on. a sample of 400 rice-producing households spread across 40 villages in 2 Chinese provinces, Section 3 then presents the SML results. While the models and methods demand a lot of the data, both investment regret and factor price equalizations effects prove significant. Interestingly, the tenure security effects

> 6 For China, recent studies by Rozelle, Li and Brandt (1998), Carter and Yao (1998), and Jacoby Li, and Rozelle (1998) find some evidence of tenure security effects, standing in contrast to the earlier study by Feder, Lau, Lin, and Luo (1992) who found that the link between fanners' perception of tenure security and farm investment was very weak if positive at all. The investment regret effect is similar to what Besley identifies as the tenns of trade effect, while the factor price equalization effect has parallels to Gavian and Fafchamps (
24 "There is an active rental market for land in rural Egypt. Thus, while 75.7 percent of the survey households own no land, in terms of land access (that is, land owned plus land rented in) a slightly smaller percentage (61.6 percent) of the survey households have no land access.

'<sup>2</sup> For example, on Pakistan see Adams (1994), on India see Walker and Ryan (1990), and on Malaysia see Shand (1986).

'<sup>3</sup> 1n analyzing whether an income source is inequality-increasing or -decreasing, it is assumed that additional increments of that income source are distributed in the same fashion as the original units. 141n fact, nonfarm income is the most equally distributed source of income in the study, because it has the lowest source Gini (Gk) in Table 4.

'As shown in Table 4, the shares of source income in total income **(Sk)** are all positive.

'<sup>6</sup> According to Table 4, the source Gini **(Gk)** for agricultural income is 1.155. The source Gini for agricultural income exceeds unity in this table because 16.5 percent of the households have negative agricultural incomes.

'<sup>7</sup> Excluding the households with no land, the Gini coefficient of landownership is 0.532.

**IsFor** an interesting effort to use cross-national data to examine the nature of the casual relationship

between income and land in a sample of 28 developing countries, see Quan (1989).
|15.7|
|2009-10|46.4|38.0|35.6|28.1|31.7|25.8|20.9|15.4|
|2011-12|47.4|37.3|33.4|28.9|31.1|24.7|21.8|17.8|

Source: Authors’ own estimate based on NSSO data. Note: OBC category was created after 1993-94.

Although the FLFP rate was highest among the ST, they experienced the largest drop in FLFP rate in rural areas, a drop of 18.6 percentage points during 1993-94 to 2011-12. Of this 18.6 point drop, 15 percentage points of the decline occurred during 2004-05 to 2011-12. Upper caste females experienced the lowest drop (12.4 percentage points). The following figure suggests that the drop which occurred between 2004-05 and 2011-12 was almost three times that of the period between 1993-94 and 2004-05. On the contrary, the largest drop in urban areas was for the SC, by 7 percentage points. For the most part, the drop that was recorded among urban social groups was also concentrated in the period of 2004-05 to 2011-12.

21
Higher rates of labor mobility will ensure that the benefits of WTO accession will be shared more widely. But this depends on how well the labor markets function.

In contrast to the more than two decades of product market reform in China, culminating in WTO accession, factor markets have received less attention until recently. In this paper we begin to redress this limitation by focusing on the factor markets where many barriers prevent a smoothly functioning market. However, unlike the recent paper by Whalley and Zhang (2004) on labor mobility and inequality in China, we embed our analysis in a disaggregated general equilibrium model which permits us to assess the potential interactions between ongoing product market reform and prospective factor market reforms. We introduce novel approaches to the modeling of farm-nonfarm and rural-urban labor mobility in China. We support these new specifications by drawing on recent econometric estimates of the relevant transfer elasticities, as well as survey-based estimates of the current extent of the labor market distortion. These estimates are incorporated into a CGE model with highly disaggregated households in both the rural and urban areas, based on newly available data from the Chinese National Bureau of Statistics. With this framework in hand, we are able to shed light on the question of how further opening up of the Chinese economy is likely to affect rural-urban inequality. We also explore the distributional consequences of lessening some of the existing factor market distortions.

Recently, there have been a number of papers on the subject of China’s WTO accession and its impact on income distribution and poverty. Anderson, Huang and Ianchovichina (2003) investigate the impact of China’s WTO accession on farmer incomes and on rural poverty using global CGE model. They incorporated a broad flavor of the rigidities of China’s rural labor markets into the standard GTAP model. However, their analysis was constrained by the use of a single representative household. At the other extreme, Chen and Ravallion (2004) apply the 2
contain more noise than the model predictions (including due to outliers).<sup>40</sup> In practice, as detailed further below, we find that for aggregate job losses, the predicted and survey measured levels from CFUWBES2 match reasonably well for both countries, supporting the stability of the proximate causal relationships estimated in Section 4 over a short time horizon. When compared with actual COVID-19 period labor force monitoring data (discussed in the subsequent paragraph), we generally find that the model-predicted estimates come closer to the actual observed groupwise distribution of job losses than the survey-measured projections do.

The second, more important validity check we perform is to examine the divergence between our model projections and a COVID-period monitoring survey of labor market impacts. For Jordan, we use the COVID-19 MENA Monitor Household Survey (CMMHH), conducted in January-March 2021 (See Figure 6, which depicts the surveys’ relative timelines). The sampling frame for this survey differs from that of the LMPS survey in that it does not include “other” nationalities (non-Jordanian, Syrian, or Palestinian). In addition, the surveys utilized different definitions of industry categories, work arrangements, and employment types. In particular, the definitions used for “permanent” jobs varies. Finally, the survey’s timing did not align perfectly with that of CFUWBES2. Therefore, one might expect some differences between the levels of PFPS job losses captured, Nonetheless, this comparison can provide an indication of the validity of our methodology.

_Figure 7: Jordan: Timeline of Surveys_

||||CFUWBES1|||CFUWB|ES2|CMMHH|, 2021||
|---|---|---|---|---|---|---|---|---|---|---|
|Apr-20|May-20|Jun-20|Jul-20<br>Aug-20|Sep-20|Oct-20|Nov-20|Dec-20|Jan-21|Feb-21|Mar-21|

Source