rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8104

# **Abstract**

Poverty estimates usually lag behind two years, which makes it difficult to provide real-time poverty analysis to assess the impact of economic crisis and shocks among the less well-off, and subsequently limits policy responses. This paper takes advantage of up-to-date average economic welfare indicators like the gross domestic product per capita and comprehensive harmonized micro data of more than 180 household surveys in 15 Latin American countries. The paper tests three commonly used poverty nowcasting methods and ranks their performance by comparing country-specific and regional poverty nowcasts with actual poverty estimates for 2003–14 period. The validation results show that the two bottom-up approaches, which simulate the performance of each agent in the economy to nowcast overall poverty, perform relatively better than the top-down approach, which uses welfare estimates to explain the performance of poverty at an aggregate level over time. The results are robust to additional sensitivity and robustness tests.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gcaruso@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
**Table 2: The Determinants of Greenfield FDI in the MENA region, Q1 2003 – Q4 2012 Baseline Regressions**

|**The Determi**|**nants of Gree**<br>_Dependent_<br>|**nfield FDI in**<br>_Variable: Log_<br>_LSDVC_<br>|**the MENA**<br>_GFI (in millions_<br>_Estimates_<br>|**region Q1 2**<br>_of USD)_<br>|**003 – Q4, 2012**<br>|<br>|
|---|---|---|---|---|---|---|
||(1)<br>Only<br>Political<br>Instability|(2)<br>Only<br>Economic<br>Controls|(3)<br>Political<br>Instability<br>and<br>Economic<br>Controls|(4)<br>Only<br>Arab<br>Spring|(5)<br>Arab Spring<br>and<br>Economic<br>Controls|(6)<br>Full<br>Specification|
|Political Instabilityt-1|-0.89***||-0.94***|||-0.86**|
||(0.30)||(0.32)|||(0.36)|
|Arab Spring Dummyt-1||||-0.94**|-0.73**|-0.31|
|||||(0.47)|(0.37)|(0.41)|
|GFIt-1|0.17***|0.16***|0.13***||0.14**|0.13**|
||(0.05)|(0.04)|(0.04)||(0.04)|(0.04)|
|ln Industrial Productiont-1||0.75*|0.76*||0.62|0.70*|
|||(0.40)|(0.39)||(0.39)|(0.39)|
|Δ ln Industrial Productiont-1||0.81|0.64||0.84|0.67|
|||(0.69)|(0.69)||(0.69)|(0.70)|
|Inflation t-1||-12.10***|-10.35***||-11.95***|-10.44***|
|||(3.42)|(3.49)||(3.43)|(3.49)|
|Δ Exchange Ratet-1||4.49|3.37||4.17|3.34|
|||(4.87)|(4.80)||(4.85)|(4.80)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|604|604|604|604|604|604|
|Number of Countries|17|17|17|17|17|17|

Bootstrapped standard errors in parentheses ***p<0.01, ** p<0.05,* p<0.10 27
2

# 11. SPATIAL PATTERN **Of** PRODUCTION **AND MARKETING** IN THE EXPORT SECTOR

4. To understand the demand for rural transport generated by the production of a crop, it is necessary to understand where the crop is produced relative to where it is consumed or exported. The essence of agricultural production is, of course, the importance of land as an input and the consequent spatial dispersion of production. The volume and location of production is determined both by the area where the crop is cultivated (the extensive margin of cultivation) and by the intensity of production on land that is used to produce the crop (the intensive margin of cultivation). The boundaries of the region in which a particular crop is grown in a particular country may be determined in various ways, by the limits of the country's borders, by the limits of the land that is at all physically suitable for cultivation of the crop, or by the limits of the land on which it is economically profitable to grow the crop. The first two factors are exogenous to the transport policies of the government, while the last is highly influenced by them.

_5._ In practice, whether one emphasizes changes in the intensive or extensive margins of cultivation in an analysis of agricultural response to transport policy depends partially on the degree of aggregation in the information that is available. At the level of individual farms, it may be possiole to see changes in the extensive margin while at a more aggregate level the boundaries of the region where production occurs may not change at all. In the case of African countries, information with which to study the effect of transport policy on agricultural response at the farm level is largely unavailable, either because it has not been collected or because governments treat it as confidential. For practical reasons, therefore, the study of rural transport is largely restricted to data reported at a geographical level just below that of the region in which production occurs (termed zones productrices in the case of the CIDT) or to special studies of particular road projects. It is the purpose of this paper to see what can be learned about rural transport from the study of zonal data.

6. The cotton sector of the Cote d'Ivoire occupies about 188,000 km<sup>2</sup> , split into approximately 56 zones that the CIDT uses for organizational and reporting purposes. It is hordered r.nn the easnt uwest antd north by the internaticnl bcundcs of . country. To "'e south it is limited by the replacement of the savannah which is agroclimatically suited to cotton by the forest which is not. For these reasons, it does not seem that an important part of the response of production to decreases in transport costs is to be found in the expansion of the borders of the cotton region, as reflected in an addition of new zones. I therefore assume that the number of zones is fixed when making calculations on the effects of changes in the (implicit)
Table 4: **The** effect of student background characteristics on student expectations, perceived parental support, perceived ability, attitudes and effort, with pretest achievement controlled, in Thailand, 1981-82

|Independent<br>variables|YHOREED|PARENC|YPERCEV|<br>YFUTURE|<br>YDESIRE|<br>YMWHK|
|---|---|---|---|---|---|---|
|Pretest<br>Sex(2 -<br>boy)|.02***<br>(11.89)<br>-.13***|<br>-.01***<br>(3.40)<br> <br>.27***|-.04***<br>(18.60)<br>-.10***|.03***<br>(14.04)<br> <br>-.08**|<br>01***<br>(9.35)<br>-.21***|<br>.06***<br>(6.40)<br> <br>.05|
||(4.28)|(8.50)|(3.50)|(2.90)|(8.77)|(.40)|
|Age|-.00|-.02|.04|.01|.03|-.01|
||(.13)|(.59)|(1.24)|(.34)|(1.22)|(.07)|
|Age squared|-.00|.00|-.00|-.00|-.00|-.00|
||(.06)|(.76)|(1.28)|(.37)|(1.31)|(.02)|
|Father's occupation|10***|<br>-.05**|<br>.03|.02|.04**|-.13|
||(5.84)|(3.03)|(1.82)|(1.30)|(2.58)|(1.71)|
|Mothers education|.10***|<br>-.06**|<br>.00|-.00|.05**|<br>.18*|
||(4.96)|(3.01)|(.05)|(.04)|(2.88)|(1.99)|
|Calculator at home|.04|.01|.00|.02|-.00|.10|
||(1.01)|(.19)|(.09)|(.52)|(.07)|(.67)|
|District PCI (1000 bh)|.00|.01|.00|-.01**|.00|-.03|
||(.68)|(1.84)|(.15)|(2.97)|(.26)|(1.60)|
|Lang. of inst.**at**home|-.04|-.15|-.15|-.12|.00|-.61|
||(.44)|(1.65)|(1.86)|(1.42)|(.03)|(1.59)|
|Dist. PCI x Lang of inst.|.00|.00|.01|.01|-.01|.03|
||(.53)|(1.80)|(1.90)|(1.06)|(1.05)|(1.10)|
|Constant|4.05|3.12|1.35|1.22|3.19|5.11|
|Adjusted R-squared|.10|.04|.09|.06|.06|.01|
|N|3626<br>3|595<br>3|597|3580|3615|3572|

***p < .001, ** p < .01, * p <.05.

Note: Unstandardized regression coefficients; t-statistics in parentheses.

- 27 -
- 17 -

reliance through higher import prices. 14/ Another result which highlights the importance of market charateristics is the significant inverse association that exists in both time period between relative prices and the number of trading partner (country) contacts. Thus, those importing countries maintaining trade relations with a larger number of exporters, and theoretically benefiting from greater competition and information on comparative prices, pay less for their exports. Unfortunately, from the view of development policy, there is evidence that the smaller, poor countries may not be able to sustain a larger number of trading contacts since this variable was significant and positively correlated with GNP per capita, market size, relative quantities purchased and the developed country dummy. 15/ Thus, developing countries acting in isolation (i.e. not resorting to practices like combined bulk purchasing) may not be able to maintain the trade contacts and other market conditions leading to lower import prices.

Somewhat surprisingly, the 1968-69 correlations fail to show a strong association between relative prices and either of the market size variables, although both these variables are significant with the expected (negative) sign in 1986-87. While import prices are negatively correlated with both the

- 14/ As might be expected a strong inverse correlation exists between the number of trade contacts and the share of the three largest suppliers. That is, countries with relatively few contacts typically receive a higher percentage of total imports from three major exporting countries.

- 15/ Some practices of developed countries, like the "tying" of international aid, may strongly reinforce the negative structure (monopoly) elements present in developing countries' import markets. Specifically, tied aid requires that recipient countries make purchases from domestic firms headquartered in the donor nation. Knowing that the country receiving tied aid cannot turn to other bidders, suppliers in the donor country have no incentive to engage in competitive international bidding. The practice of tying aid may be an important reason for the sizeable and persistent premiums that the associated countries' paid over 1962-1987.
**The Ecological Impact of Transportation Infrastructure**<sup>_∗_</sup> Sam Asher<sup>_†_</sup>

Teevrat Garg<sup>_‡_</sup> Paul Novosad<sup>_§_</sup> June 2018

> _∗_ We are grateful for feedback on earlier versions of this paper from Prashant Bharadwaj, Jenn Burney, Jonah Busch, Eric Edmonds, Paul Ferraro, Gordon Hanson, Maulik Jagnani, Erzo Luttmer, Erin Mansur, Gordon McCord, and seminar participants at UC San Diego, George Washington University, and the 2017 CU Boulder Environmental and Energy Economics Workshop. Ryu Matsuura provided excellent research assistance.

> _†_ World Bank, sasher@worldbank.org

> _‡_ UC San Diego, teevrat@ucsd.edu

> _§_ Dartmouth College, paul.novosad@dartmouth.edu
<!-- Start of picture text --> Figure 17. Impact of increasing social transfers instead of reducing the PIT rate<br>A. Gini Coefficient B. Poverty Headcount rate<br>(US$5.50 PPP)<br>0.380 18<br>15.4<br>0.370 16 14.8<br>13.9 14.0<br>0.360 14 12.111.8<br>0.350 12 10.8<br>0.340 10<br>0.330 8<br>0.320 6<br>0.310 4<br>0.300 2<br>Market Disposable Consumable Final Incom 0<br>Income + Income Income Market income + Disposable income Consumable<br>Pensions pensions income<br>Baseline<br>2016 Baseline<br>Simulation PIT rate=10% & VAT 18%<br>Decline in both PIT and VAT<br>Simulation: Social benefits increase by 1/2 of cost of P Social benefits increase by 1/2 of cost of PIT reduction<br>reduction<br>Source: World Bank estimates based on Romanian HBS (2016).<br><!-- End of picture text -->

# **VII. Conclusions**

Romania has high levels of poverty and inequality relative to other EU countries. In this context, the combined effect of taxes and social spending in Romania helps to reduce inequality, although less so than other EU countries. Direct taxes and transfers are progressive and redistributive, more so than other developing countries, but less than other European countries such as Poland and Croatia. In contrast, indirect taxes are regressive and unequalizing. In-kind transfers are progressive and equalizing, particularly spending on primary and lower secondary education, and spending on the minimum health benefit. However, spending on primary education is much less equalizing than in other developing countries, while health spending is not necessarily pro-poor.

The impact of taxes and transfers on poverty is much worse. When we compare the poverty rate before and after taxes and transfers, we find that poverty is higher after all taxes and cash transfers. The main reason for this is that taxes are poverty increasing, so much so that the poverty-reducing power of direct transfers is completely wiped out once indirect taxes are included in the analysis. In fact, households beginning in the second decile were net payers to the treasury in 2016, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population. The main problem is that direct cash transfers to poor households (in the form of social assistance and non-contributory benefits) are not large enough to compensate them for the burden of indirect taxes. This is especially important for rural households and for families with children.

However, things are looking up for Romania, as growth has been strong, and unemployment reached its lowest level in more than 20 years in 2017. In this context, fiscal policy has turned pro-cyclical, driven by significant tax cuts and wage and pension increases which has placed Romania on a trajectory towards re27
Standardized Precipitation Index (SPI) from the AFDM system. The SPI is calculated using data from the bias-corrected TMPA and hybrid observational reanalysis,<sup>5</sup> including data from 1950 to 2015. The SPI-1 is defined across districts and over time following an aggregation and standardization process analogous to the one employed for the rainfall and windspeed Z-scores. For ease of interpretation, the SPI district-level Z-scores are multiplied by negative -1 to invert the scale.<sup>6</sup> The graph at the bottom of Figure 2 shows the distribution of the SPI during the period of reference.

# **3. Results**

# _3.1 Household consumption and poverty status_

Evidence available in the literature illustrates the high levels of income and consumption variability in risky agricultural contexts (Dercon 2002). We first focus on the effects of exposure to each of the three weather shocks on food and total per capita consumption, a traditional measure of household monetary welfare. Since subsistence farming is widespread across Mozambique, the variable used to measure household food consumption includes not only the actual expenditures incurred in purchasing food items, both for food eaten at home and outside, but also selfconsumption. Consumption from gifts and wages in-kind is also included in the calculation of the consumption aggregate. Consumption levels are valued using price data obtained from surveys of local markets that were fielded around the same time as the household survey. As noted above, all specifications include districts fixed effects, year fixed effects, agricultural growing cycle fixed effects, gender fixed effects (for individual-level regressions), and control variables such as age and urban status.

The first set of results of the triple difference estimation (equation 1) is presented in Table 1. The coefficients are expressed as the effect on the dependent variable of a change of one standard deviation in the shock intensity measure (Z-score). One interesting pattern in the findings is that the three shocks pushed exposed households to cut back on their food consumption – all the point

> 5 Bias-correction and hybrid observational reanalysis methods are widely used to improve the accuracy of satellite data products by matching the moments of satellite observations with station observations where both types of data are available. 6 Negative SPI indicates lack of precipitation, thus, a negative SPI with a large absolute value is associated with a more severe drought intensity. In order to be consistent with the other disasters interpretation (interpreting our coefficients as the impact of disasters as intensity increases) we multiplied the intensity by -1.

11
# **Reference**

- Acemoglu, Daron. 2010. “Theory, General Equilibrium, and Political Economy in Development Economics.” Journal of Economic Perspectives 24(3), 17-32.

- Anderson, R.C., D. M. Reeb. 2008. “Founding-Family Ownership and Firm Performance.” _Journal of Finance_ LVIII (3), 1301-28.

- Alfaro, L. 2016. “Gains from foreign direct investment: Macro and micro approaches.” _World Bank Economic Review_ 30, S2-S15.

- Acs, Zoltan J., David B. Audretsch. 1988. “Innovation in large and small firms: An empirical analysis.” _American Economic Review_ 78(4), 678-690.

- Ahern, K., A K Dittmar. 2012. “The changing of boards: The impact on firm valuation of mandated female board representation.” _Quarterly J. of Economics_ 127, 137-197.

- Amore, M.D., O. Garofalo. 2016. “Executive gender, competitive pressures, and corporate performance.” _Journal of Economic Behavior and Organization_ 131, 208-327.

- Becker, G., HG Lewis. 1973. “On the interaction between the quantity and quality of children.” _Journal of Political Economy_ 81 (2, part 2), S279-288.

- Bennouri, M., T. Chtioui, H. Nagati, M. Nekhili. 2018. “Female board directorship and firm performance: What really matters?” _Journal of Banking and Finance_ 88, 267291.

- Bertrand, M. 2011. “New perspectives on gender.” _In_ Handbook of Labor Economics volume 4b. p 1543-1590.

- Berger, A.N., T. Kick, and K. Schaeck. “Executive board composition and bank risk taking.” _Journal of Corporate Finance_ 28, 48-65.

- Blau, F., and L.M. Kahn. 2017. “The gender wage gap: Extent, trends, and explanations.” _Journal of Economic Literature_ 55(3), 789-865.

- Bonte, W., M. Piegeler. 2013. “Gender gap in latent and nascent entrepreneurship: Driven by competitiveness.” _Small Business Economics_ 41: 961-987.

- Bruhn, M. 2009. “Female-owned firms in Latin America: Characteristics, performance, and obstacles to growth.” World Bank Policy Research Working Papers 5122.

- Buera, F.J., J.P. Kaboski, M.Q. Zhao. 2013. “The rise of services: The role of skills, scale, and female labor supply.” _NBER Working Paper No._ 19372 _._

- Calderon, G., Iacovone, L. and Juarez, L. (2017). “Opportunity versus Necessity: Understanding the Heterogeenity of Female Micro-Entrepreneurs,” _World Bank Economic Review_ 30, 80-96.

- Campos, F., Coleman, R., Conconi, A., Donald, A., Gassier, M., Goldstein, M., Chavez, Z., Mikulski, J., Milazao, A. Paryavi, M., Pierotti, R., O’Sullivan, M., and Vaillant, J. 2019. “Profiting from parity: Unlocking the potential of women’s business in Africa.” Main Report (English), Washington, D.C.: World Bank Group.

- Croson, R., U. Gneezy. 2009. “Gender differences in preferences.” _Journal of Economic Literature_ 47(2), 448-474.

- De Mel, Suresh, David McKenzie, and Christopher Woodruff. 2008. “Returns to capital in microenterprises: Evidence from a field experiment.” _Quarterly Journal of Economics_ 123(4), 1329-1372.

19
firm to track respondents who moved since the time of the initial rollout of the program as long as the household/respondent has moved within the same municipality or if she/he has moved to one of the main economic centers in the Philippines, such as, metro Manila, Cebu, Davao and Cagayan de Oro. About 29% of our sample have moved to major cities. In instances, where the young adult is married or is living with a partner, we also interviewed the partner. Note that the household questionnaire was administered to the current, not original household.

Based on our power calculations, our target was to interview 1,576 young adults: 438 beneficiary women, 438 beneficiary men, and 700 partners in 876 households. Early stages of fieldwork proved that we miscalculated the co-habitation rate in the Philippines. To ensure that we have sufficiently large sample of partners, we increased the number of young adults’ households to 932. However, largely because the co-habitation rate was lower than originally programmed, our final sample is 87% of the target sample: 1,372 respondents, 870 of them are former beneficiaries of the 4P program, 502 partners of the former beneficiaries. This work focuses on the sample of former beneficiaries.

We collected the data between October 2019 and January 2020 from 932 young adult households, spread across approximately 71 barangays. The survey collected extensive data using two questionnaires: household and individual. The household questionnaire included modules on (i) basic demographic characteristics, (ii) economic participation, (iii) housing conditions, (iv) asset ownership, and (v) participation in the 4P program. The individual questionnaire includes modules on (i) decision making, (ii) time use, (iii) self-efficacy, (iv) well-being, (v) social capital, (vi) attitudes towards gender norms, genderbased violence and violence against children, (vii) pregnancy history, (viii) exposure to gender-based violence, and (ix) exposure to transactional sex. The last three modules are only administered to the women in the sample. The sub-sample of female respondents was randomly split in two groups for sections 8 and 9: half of the sample was interviewed using Face-to-Face Direct Questioning (FTFDQ) technique, the other half responded to the same question in Audio Computer Assisted Self-Interview (ACASI). As we do not find differences in prevalence rates reported via FTFDQ vs. ACASI,<sup>3</sup> we pool the responses in the current analysis, but control for the type of the interview.

When collecting data on GBV, we followed WHO recommendations in elaborating data collection protocols, including working with same-sex enumerators, specialized training for enumerators, protocol

> 3 Detailed results are available upon request.

9