rafmacalaba/datause-encoder-data huggingface dataset:
# PPR Working Paper Series

|||_AhorDAM_||Contact<br>forpaper|
|---|---|---|---|---|
|WPS307|On the Accuracy of Economic<br>Observations: Do Sub-Saharan<br>Trade Statistics Mean Anything|Alexander J. Yeats|November 1989|J. Epps<br>33710|
|WPS308|Harmonizing Tax Policies in Central<br>America|Yalcin M. Baran|November 1989|T. Watana<br>31882|
|WPS309|Honduras-Public Sector Finances.<br>Issues, and Policy Suggestions<br>to Resolve Them|Yalcin M. Baran|November 1989|T. Watana<br>31882|
|WPS310|A Macroeconomic Consistency<br>Framework for Zimbabwe|Ali Khadr<br>Klaus Schmidt-Hebbel|||
|WPS311|Macroeconomic Performance<br>Before and After Disinflation in<br>Israel|Leonardo Leiderman<br>Nissan Liviatan|November 1989|R. Luz<br>61588|
|WPS312|Improving Public Enterprise<br>Performance: Lessons from<br>South Korea|Mary M. Shirley|October 1989|R. Malcolm<br>61708|
|WPS313|The Evolution of Paradigms of<br>Environmental Management in<br>Development|Michael E. Colby|November 1989|C. Evangelista<br>32645|
|WPS314|Primary Commodity Prices and<br>Macroeconomic Variables: A Long-<br>run Relationship|Theodosios Palaskas<br>Panos Varangis|November 1989|D. Gustafson<br>33714|
|WPS315|Notes on Patents, Distortions,<br>and Development|Julio Nogu6s|||
|WPS316|The Macroeconomics<br>of Populism<br>in Latin America|Rudiger Dornbusch<br>Sebastian Edwards|||
|WPS317|Price and Quality Competitiveness<br>of Socialist Countries' Exports|Zdenek Drabek<br>Andrzej Olechowski|||
|WPS318|Debt Buybacks Can Indicate<br>Sovereign Countries' Willingness<br>to Raise Investment and Repay<br>Debt When Partial Debt Relief is<br>Offered: Theory and Tests|Sankarshan<br>Acharya<br>Ishac Diwan|||
|WPS319|Trends in South-South Trade and<br>the Potential in Non-Discriminatory<br>Liberalization of Barriers|Refik Erzan<br>|||
Table 4: Effects of exposure to COVID-19, all sample

||Wage<br>aspiration|Wage<br>expectation|Aspiration<br>gap|Unrealistic<br>high aspiration|Realistic<br>aspiration|Unrealistic<br>low aspiration|Willing to<br>migrate|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|COVID shock|-4,516.319**|-2,401.699*|-2,114.620**|-0.263***|0.178|0.084|-0.238**|
||[1,577.856]|[1,314.336]|[818.856]|[0.069]|[0.157]|[0.140]|[0.090]|
|Outcome mean|19,740.57|17,005.66|2,734.91|0.52|0.26|0.23|0.48|
|Observations|3,180|3,180|3,180|3,180|3,180|3,180|3,143|

_Notes:_ All columns include the full set of controls mentioned in specification (1). Robust standard errors clustered by ITI district are in brackets. Variable definitions are in the online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

literature has established that the pandemic had a detrimental impact on migrants from rural to urban areas in India. As the unexpected lockdown measures were implemented, workers returned to their rural homes and experienced declines in salaries, consumption, and wellbeing; female workers were particularly vulnerable to this abrupt displacement (Allard et al., 2022). While affected men were, on average, able to subsequently remigrate and recover all of their pre-pandemic income, female re-migrants continued to experience lower income and employment (Arora et al., 2023). To the extent that young and inexperienced job-seekers form their wage expectations and aspirations on the basis of prevailing labor market conditions, the pandemic’s negative impact on female migrants could be a potential channel for our findings. Therefore, in column (7) of Table 3, we analyze whether exposure to the pandemic reduced women’s willingness to migrate for work, especially for rural women. We observe that the pandemic reduced rural women’s willingness to migrate for work by 65 percent, whereas the effect on urban women was indistinguishable from zero. This decline in the desire to migrate could be driven by a decrease in the expected net benefit from migration due to greater uncertainty, fear of job loss, lower chances of reintegration, and lack of social security in the event of another COVID-19 wave.

# **4.1 Robustness Checks**

Table 5 demonstrates the robustness of our findings to using a more aggregate measure of pandemic exposure, i.e., the district-level COVID-19 death rate. These regressions are based on a modified version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population that lives in urban areas, share of population that belongs to a Scheduled caste, share of population that is female, share of population that lives in slums, and total district population. We also control for the all-cause mortality rate in 2019 at the district level from 8
Policy Research Working Paper 6681

# **Abstract**

In Africa, farmers have been reluctant to take up new varieties of staple crops developed to boost smallholder yields and rural incomes. Low fertilizer use is often mentioned as a proximate cause, but some believe the problem originates with incomplete input markets. As a remedy, African governments have introduced technology adoption programs with fertilizer subsidies as a core component. Still, the links between market performance and choices about using fertilizer are poorly articulated in empirical studies and policy discussions, making it difficult to judge whether the programs are expected to generate lasting benefits or to simply offset high fertilizer prices. This paper develops a conceptual model to show how choices made by agents supplying input services combine with household livelihood settings to generate heterogeneous decisions about fertilizer use. An applied model is estimated with data from a panel survey in rural Ethiopia. The results suggest that adverse market conditions limit the adoption of fertilizer-based technologies, especially among resource-poor households. Farmers appear to respond to market signals in the aggregate and this provides a pathway for subsidies to stimulate demand. However, the research suggests that lowering transaction costs, through investments in infrastructure and market institutions, can generate deeper effects by expanding the technologies available to farmers across all pricing outcomes.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 dlarson@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
82

|COUNTRY|**PROJECT**|LEGAL COMPONENT|FINANCIERS|
|---|---|---|---|
|Uruguay<br>(Cont.)|Second Agricultural Development<br>Project (Loan No. 3131 UR),<br>November 20, 1989<br>Japanese Grant Agreement,<br>November 20, 1989|Review of the legal constraints to agricultural export trade.|IBRD $65 m.<br>Japanese Grant Y250 m.|
||Public Enterprise Reform Project<br>(Loan No. 3517 UR), September 19,<br>1992|Legal technical assistance in connecticn with: (a) the<br>privatization of the national airline company (PLUNA); (b)<br>the reform of the port sector; (c) the reform of the power<br>sector;and (d)theprivatization of some alcohol plants.|IBRD $11 m.|
||Public Sector Reform Project,<br>Japanese Grant Agreement,<br>November 20, 1991|Legal technical assistance in connection with a reform of the<br>legal and regulatory framework of the electric power sector.|Japanese Grant V108.9 m.|
||Public Enterprise Reform and<br>Privatization Loan,<br>Japanese Grant Agreement,<br>March 23, 1992|Legal technical assistance in connection with: (a) the planning<br>and implementation of the participation of private investors in<br>a new telecommunications company; and (b) the<br>establishment of a regulatory authority for the<br>telecommunications sector, including drafting of regulations.|Japanese Grant 1209.3 m.|
||Private Sector Development Project<br>(Loan No. 3698 UR) (proposed)|Assistance in carrying out studies: (a) to expand the use of<br>movable collateral on financial transactions (including a legal<br>and economic analysis of the impediment to attach movable<br>collateral and the sale of such collateral), and (b) to<br>modernize and computerize the cattle registry, and integrate<br>such registry with the legal registries.|[BRD_$35_m.|
||Natural Resources Management and<br>Irrigation Development Project<br>(Loan No. 3697 UR), March 4,<br>1994|Study of the legal framework concerning the use of irrigation<br>water.|IBRD $41 m.|
23 both equations with the variable included in the selection equation makes the total effect of that variable appear negative. Similarly, the conditional marginal effects for the highest level of men’s education, the dummy for an indigenous head of household, and the dummy for electricity connection in Guatemala; and the price of kerosene, the number of bedrooms, and the highest level of education for women in Sri Lanka arise solely from indirect effects through the value of rho and have the opposite sign to what would have been expected for the directly obtained coefficient.

# **4.4 Econometric analysis using national average prices**

Analysis using surveys without quantitative information on fuel use is limiting, but to the extent that it reproduces the findings in section 4.3, such analysis can strengthen the conclusions drawn. The numbers in this section need to be treated with care, because they are subject to an even greater possibility of being biased on account of additional omitted variables.

The same independent variables, barring household-level fuel prices, are examined in the four countries without quantity information. Because household-level prices are not available, conditional and unconditional marginal effects are not calculated for these countries. Brazil is treated differently because, at 89 percent, LPG use in urban areas in Brazil is already high, and the share rises to 92 percent if natural gas users are added. Virtually all of the remaining 8 percent of households had electricity connection, many of whom could have been using electricity for cooking. Therefore, the two-stage Heckman estimation in Brazil focuses only on rural households, while a regression equation is tested for consumption for urban households entering only user households. The results are summarized in Table 8.

Table 8: Heckman model in countries with national average prices

|**Independent variable**|**Albania**|**Brazil rural**|**Brazil urban**|**Mexico**|**Peru**|
|---|---|---|---|---|---|
||**Select**<br>|**ion equation(**<br>|**probit)**|||
|Log of household expenditure|0.37<br>(0.055)|0.35<br>(0.025)||0.24<br>(0.014)|0.69<br>(0.028)|
|Number of rooms<sup>a</sup>|0.12|0.040||0.021|0.017|
||(0.027)|(0.010)||(0.011)|(0.008)|
|Household size|0.14|–0.088||0.11|–0.16|
||(0.046)|(0.010)||(0.008)|(0.007)|
|Household size squared|–0.015|—||–0.004|—|
||(0.004)|||(0.0006)||
|Dummy for engaging in|—|UA||–0.32|–0.52|
|agriculture||||(0.031)|(0.035)|
|Highest level of education for|—|0.031||—|0.020|
|men||(0.005)|||(0.003)|
|Highest level of education for|—|0.029||—|0.036|
|women||(0.005)|||(0.003)|
|Age of household head|–0.005|—||0.005|—|
||(0.002)|||(0.0006)||
|Dummy for male household|0.64|—||—|0.15|
|head|(0.085)||||(0.035)|
|Dummy for indigenous|UA|–0.33||–0.63|—|
|household head||(0.14)||(0.029)||
|Dummy for urban residence|0.46|NA||0.17|0.69|
||(0.050)|||(0.023)|(0.035)|
dence of a significant change in women’s attitudes toward IPV in response to increased exposure to female leaders. In addition, we do not observe any significant impacts on more subjective evaluations of IPV, including women’s likelihood of reporting being subjected to psychological violence.<sup>3</sup> Our study contributes to a growing literature that documents the effects of female leaders on a range of health and economic outcomes, including infant mortality and health-seeking behaviors (Bhalotra and Clots-Figueras 2014), local public goods and female-oriented policies (Chattopadhyay and Duflo 2004; Clots-Figueras 2011; Bardhan et al. 2010), attitudes toward female leaders (Beaman et al. 2009), and aspirations and education of girls (Beaman et al. 2012; Clots-Figueras 2011).<sup>4</sup> We complement this literature by showing that female leaders improve village-level public health infrastructure, especially for services that benefit women directly. However, unlike prior studies, we also find that female leaders may have unintended negative consequences for their female constituents if their policy formulations lead to intrahousehold conflict.

Our work also relates to studies documenting that female leaders induce an increase in the reporting of crimes against women, such as rape and kidnapping, to the police (Iyer et al. 2012) and a decrease in women’s acceptance of spousal violence (Kuipers 2020). Although our findings complement these studies, our work differs in several ways. First, we focus on women’s experience of violence based on survey data collected by female surveyors in a confidential setting rather than data from police reports. Given the social norms and stigma surrounding IPV in our context, women are more likely to report IPV in a survey context than to the police. Second, we focus on IPV rather than crimes against women more generally. IPV is not only more prevalent than other types of gender-based violence, but the mechanisms underlying IPV are also likely to be different. Our study sheds light on several channels through which IPV against women may respond to increased exposure to female leaders.

Moreover, our work relates to a limited literature showing that women’s improved access to family planning services may lead to intrahousehold conflict and IPV (Ashraf et al. 2014; Anukriti 2020). More generally, we contribute to an emerging literature on

> 3While administrative data on IPV, such as police or hospital reports, represent more objective measures of IPV, the restricted access of abused women in developing countries to police stations and hospitals also raises concerns about the reliability of such data. For instance, we observe that in our data only 0.6 percent of physically abused women filed a police report and that only 0.1 percent of them visited a doctor or medical personnel.

> 4A broader literature explores the role of increased political representation for minority groups on the provision of publicly provided goods and services. For instance, greater representation of Scheduled Castes in India has been shown to increase public goods provision for these castes (Pande 2003).

3
of private sector banks, India’s banking system is still largely state dominated. As of 2012, India’s state owned banks have 73% of market share of assets and 83% of branches.<sup>14</sup> Even within this government dominated banking sector, there is a large variation across India’s states in level of financial development. Bajpai and Sachs (1999) note that there has been a wide variation in the adoption of economic reforms with states like Maharashtra being very reform oriented while others, especially the poorest BIMARU<sup>15</sup> states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) being laggards. Aghion et al. (2005) also note the reforms in the 1990s to be associated with increasing cross-state inequality in industrial performance.

# **Insert Table 1 here**

Our measure of financial development is the ratio of total Commercial Bank Credit outstanding to the Net State Domestic Product (SDP) in each census year and gauges the depth of financial development. The data is sourced from Burgess and Pande (2005) with updates from the Reserve Bank of India (http://dbie.rbi.org.in). According to statistics by the Reserve Bank of India, in March 2006, there were about 217 commercial banks in operation, with 161 of them state owned (includes nationalized banks and regional rural banks), 27 private sector banks and the rest 29 foreign banks.<sup>16</sup> Despite the entry of private banks, state controlled banks still account for three-quarters of all loans in India. India’s largest commercial bank, the State Bank of India, is over two hundred years old and is government owned. Banerjee, Cole, and Duflo (2004) state that 83% of banking business in India is through government owned banks and even the private banks are subject to extensive regulations on who they can lend to.

> 14 Speech by Dr Duvvuri Subbarao, Governor of the Reserve Bank of India, at the FICCI–IBA (Federation of Indian Chambers of Commerce & Industry – Indian Banks’ Association) Annual Banking Conference, Mumbai, 13 August 2013. http://www.bis.org/review/r130813b.pdf?frames=0.

> 15 BIMARU resembles the Hindi word “Bimar” meaning sick.

> 16 <u>http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/78903.pdf</u>

13
13 change in the housing stock." Although users may wish to reduce district heat consumption, they cannot. The invariance of the quantity consumed to the price also means that there will be no incentive or ability for consumers to insulate houses and engage in activities to reduce the needed district heat quantities. Raising the price of coal on the other hand _will_ have direct effects on consumption. This is evident from the fall in coal consumption after the price hikes in 1990. For power, the price elasticities may also be lower than in the market economies. Because of poor technology, appliances in the central European countries use much more electricity then their European counterparts. For example about 30 percent of the stock of TVs in Poland are old Polish-made black and white models which use more than three times as much as models imported from OECD countries (Poland: Household Budget Survey, 1993 and Meyers, Schipper, and Salay, 1994). In the last 20 years more homes have become owners of electrical appliances as well (Meyers, Schipper and Salay, 1994). Turning over this stock of out-dated and energy consuming appliances will take time, and until newer and more efficient ones replace them, one can expect price elasticities of demand to remain lower than in market economies.

In section III, we simulate the impacts of raising energy prices for the three major energy sources (district heat, electricity and gas) on a cross-section of household groups, under a range of assumptions about price elasticities. Our assumed range of price elasticities of demand for energy is between zero and minus one. This is based on estimates from previous work: Pindyck finds the long run elasticity for "energy" as a whole to be -1. 1 in the U.S. and others have found similar estimates for many European nations, ranging from -0.28 to -1.7 (Pindyck p. 118). Estimates of own-price elasticities for _specificfuels_ have led to more divergent results. Though the evidence is scant, price elasticities tend to be slightly lower in the developing countries than in the industrialized countries (Pindyck, p. 257). A survey of estimates of residential demand in three developing countries finds that long run price elasticities are slightly lower, averaging about -0.88 (Dahl 1991 p. 46). Both Pindyck 11/ Over the long run, expenditures on housing and energy may fall: in this case there is more money to spend on other goods and utility may increase.
# Table S5. Model With Interaction Between Inequality and Initial Income (Additional Controls)

|Dependent Variable is:|ln|(yit)||
|---|---|---|---|
||(1)|(2)|(3)|
|Inequality Variable is:|Market Gini<br>Solt (2015)|Net Gini<br>Solt (2015)|Gini<br>Brueckner et al. (2015)|
|Inequalityit|14.27***<br>(1.97)|9.33***<br>(1.17)|8.29***<br>(1.07)|
|Inequalityit|-1.98***|-1.27***|-1.10***|
|*ln(yit-1)|(0.27)|(0.16)|(0.13)|
|ln(yit-1)|0.56***<br>(0.07)|0.66***<br>(0.04)|0.64***<br>(0.06)|
|OPSit|2.72***<br>(0.65)|2.23***<br>(0.66)|2.14**<br>(0.98)|
|TWWIit|0.16***<br>(0.05)|0.15***<br>(0.05)|0.24***<br>(0.09)|
|Investment/GDPit|0.68***<br>(0.23)|0.96***<br>(0.20)|1.11***<br>(0.20)|
|Average Years of Schoolingit|0.01<br>(0.02)|0.05**<br>(0.02)|-0.01<br>(0.02)|
|PopulationGrowthit|0.53<br>(0.57)|0.03<br>(0.56)|-0.98*<br>(0.52)|
|Relative Price of Investmentit|-0.01<br>(0.06)|0.01<br>(0.02)|0.00<br>(0.04)|
|Kleibergen Paap F-Statistic|113|278|345|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|543|543|398|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

49
||**Material**|**s Material**|**s**<sup>**Teacher**</sup><br>**supports**|<br><br>**Teacher**<br>**support**|<br>**s **<sup>**Pedagog**</sup>|<sup>**y Pedagog**</sup>|<sup>**y Life skill**</sup>|<sup>**s Life skill**</sup>|<sup>**s Foundationa**</sup><br>**skills**|<sup>**l**</sup><br>**Foundationa**<br>**skills**|**l**<br>**Multi-**<br>**disciplina**|**ry**<br>**Multi-**<br>**disciplinary**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Teacher status (teacher**<br>**omit.)**|||||||||||||
|Senior teacher|0.619|0.392*|1.048|1.515|1.230|1.122|1.031|1.162|0.995|0.850|0.509|0.566|
||(0.188)|(0.170)|(0.937)|(1.368)|(0.362)|(0.338)|(0.763)|(1.032)|(0.630)|(0.503)|(0.208)|(0.178)|
|Expert teacher|1.678|0.828|1.049|1.682|0.451|0.281|0.941|1.148|0.713|0.669|0.509|0.370|
||(1.058)|(0.636)|(0.946)|(1.145)|(0.264)|(0.189)|(0.935)|(1.387)|(0.614)|(0.718)|(0.306)|(0.201)|
|**Differences in date**|1.011|0.975|0.948**|1.081|0.783*|0.699*|1.022|1.026|1.212|1.588|1.043|1.033|
||(0.009)|(0.062)|(0.019)|(0.068)|(0.088)|(0.104)|(0.058)|(0.041)|(0.392)|(0.673)|(0.087)|(0.077)|
|**Mean asset index**||1.194||1.786||0.464||1.628||0.494||0.661|
|||(0.517)||(1.151)||(0.193)||(0.894)||(0.384)||(0.234)|
|**% Mother secondary**||0.991||0.976||1.004||0.993||0.966***||0.989|
|||(0.016)||(0.016)||(0.006)||(0.010)||(0.009)||(0.009)|
|**% Father secondary**||1.022||1.051*||1.008||0.989||1.030||0.989|
|||(0.025)||(0.021)||(0.013)||(0.015)||(0.026)||(0.012)|
|**% Mother higher ed.**||0.990||0.969||0.991||0.996||0.983||1.001|
|||(0.018)||(0.017)||(0.008)||(0.015)||(0.011)||(0.011)|
|**% Father higher ed.**||1.023||1.057*||1.013||1.002||1.018||0.994|
|||(0.025)||(0.025)||(0.012)||(0.018)||(0.025)||(0.010)|
|**% Mother work**||1.001||1.002||0.992||1.002||0.991||1.001|
|||(0.016)||(0.018)||(0.009)||(0.011)||(0.016)||(0.009)|
|**% Father professional**||0.982||0.985*||1.002||1.000||0.997||0.988|
|||(0.009)||(0.007)||(0.005)||(0.010)||(0.012)||(0.009)|
|**% Father sales**||0.986||1.006||1.007||1.027||0.989||0.990|
|||(0.020)||(0.021)||(0.007)||(0.030)||(0.017)||(0.010)|
|**N (obs.)**|331|287|331|258|331|287|310|273|310|273|310|273|
|**Pseudo R-sq.**|0.077|0.155|0.174|0.214|0.071|0.126|0.078|0.113|0.062|0.129|0.112|0.133|

_Source:_ Authors’ calculations _Notes:_ *p<0.05; **p<0.01; ***p<0.001. Odds ratios in cells, standard errors in parentheses clustered on the enumerator level. Private is a perfect predictor of consistency in the teacher supports model with SES.

19