rafmacalaba/datause-encoder-data huggingface dataset:
husband discuss it?”, “did you agree?” and “whose view eventually prevailed?”. Recently, studies in other development fields have started moving in this direction, supplementing decision-making questions or tailoring response options to give a better sense of the decision-making process. In a cross-country study of a multi-pronged poverty program, for example, Banerjee et al. (2015), ask whether respondents have a “major influence” or “no or minor influence” in the final decision across different types of expenses (food, clothing, health, etc.). Heath (2014) examines possible mechanisms on how extra earnings affect household bargaining and the threat of domestic violence in Bangladesh, and asks respondents how frequently their husbands consult with them about different household decisions (never/sometimes/often/always). In terms of cross-country surveys, the main innovation on this front has occurred in the WEAI, which asks respondents who normally takes decisions over different household domains (see Box 9 below, and the Appendix for more detail), but also asks respondents the extent to which they feel they can make their own personal decisions over each domain, _if they wanted to_ .<sup>17</sup> Moreover, it asks about the extent of input respondents have over domains, not just focusing on the final decision-maker.

|**Box 9**<br>**Women’s Empowerment in Agriculture Index (WEAI)**<br>Adult men and women were asked the following sets of questions:<br>Decision-making questions on agriculture, borrowing, and productive assets:<br>(1) For different agricultural activities (Food crop farming, cash crop farming, livestock, nonfarm<br>economic activities, wage and salary employment, fishing):<br>(a) Did you participate? (Y/N);<br>(b) How much input did you have for each agricultural activity (and separately, income generated<br>from each activity)? (No input or input in few decisions, input into some decisions, input into most<br>or all decisions, or no decision made)|
|---|
|(2) For different household decisions over agriculture, other nonfarm employment, expenditures and<br>family planning:|
|(a) who is it that normally takes the decision (see Appendix A for more detail),<br>(b) to what extent do you feel like you can make your own personal decisions regarding these aspects<br>of household life ifyou want(ed)to(not at all, small extent, medium extent, to a high extent)?|

Available evidence suggests that the _process_ of decision-making – over and above the final outcome – has important implications for how to rank responses to decision-making questions, and therefore for measuring women’s agency. For example, a cross-country study in Ecuador, Uganda, and the

> 17 The WEAI also includes questions on decisions to sell, give away, mortaging and purchasing items to determine control over productive capital – these are covered in Doss et al. (2016).

32
WPS3842

# **Does Migration Reshape Expenditures in Rural Households?**

**Evidence from Mexico**

## **J. Edward Taylor University of California, Davis**

**Jorge Mora El Colegio de Mexico**

Key Words: Expenditures, Demand, Migration, Mexico, Remittances

## World Bank Policy Research Working Paper 3842, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The authors are, respectively, Professor of Agricultural and Resource Economics at the University of California, Davis, and Doctoral Candidate at El Colegio de Mexico. Contact: J. Edward Taylor, Department of Agricultural and Resource Economics, University of California, Davis, CA 95616 (jetaylor@ucdavis.edu). Taylor is a member of the Giannini Foundation of Agricultural Economics.
**manufacturing**

|nfccredit/<br>pnfscredit|0.4263***|2.5609***|2.2844**|3.4730*|0.6941**|4.2245*|
|---|---|---|---|---|---|---|
||(0.000)|(0.009)|(0.017)|(0.072)|(0.020)|(0.077)|
|(nfccredit|||||||
|pnfscredit)<sup>2</sup>||-0.0165**|-0.0144**|-0.0223||-0.0269|
|||(0.029)|(0.046)|(0.116)||(0.125)|
|fdi|-0.2064|-0.1667|-0.0787|-0.0566|-0.7064**|-0.3821|
||(0.362)|(0.437)|(0.698)|(0.864)|(0.025)|(0.140)|
|enrolment|0.1855*|0.1439|0.1177|0.2258|0.2395**|0.1339|
||(0.063)|(0.163)|(0.256)|(0.109)|(0.043)|(0.335)|
|initialgdppc|-13.32***|-13.61***|-11.81***|-14.01***|-15.26***|-15.24***|
||(0.000)|(0.000)|(0.000)|(0.004)|(0.001)|(0.001)|
|financialcrisis|||-18.4604***|-21.6409***|||
||||(0.000)|(0.000)|||
|inflation|||||-0.3592**|-0.1899|
||||||(0.039)|(0.223)|
|Constant|101.92***|42.30|39.13|6.72|102.47**|-0.4252|
||(0.000)|(0.102)|(0.133)|(0.898)|(0.011)|(0.995)|
|# of Obs.|147|147|145|145|142|142|
|R-squared|0.358|0.378|0.435||||
|Prob > F|0.000|0.000|0.000|0.000|0.008|0.000|
|# of countries||||36|36|36|

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

27
assume that causation runs from inflation to money growth. This interpretation is supported by the fact that changes in deposits are correlated with inflation; the coefficient is 0.26, while the correlation coefficient for currency is only -0.03.

Lagged cereal price inflation enters at the first lag with a positive coefficient (0.30) and the fifth lag with a negative coefficient (-0.22). This shows there is substantial inflation inertia, as roughly 30 percent of past inflation is carried over into the next month during for about four months. It is common to find inertia in analyses of cereal markets. Although this is not well understood, the most likely explanation is that lagged inflation terms capture inflation expectations related to news, generated by shocks to food supply (Osborne, 2004). Note also that the presence of inertia and an error correction term implies that there is price overshooting, which means that an exogenous shock initially increases the price level above its long-run equilibrium level.

We fail to find that international fertilizer and energy price inflation affect cereal inflation. In the case of fertilizer, this is probably because of the small use by the majority of rural households. Alternatively, an index specified for Ethiopia might provide more information, but unfortunately the monthly data on the value and volume of fertilizer imports are not detailed enough to allow sensible calculations of unit values. The lack of significance of energy price inflation might be because the cost of fuel is small in Ethiopian agriculture, but it could also be due to government control of fuel prices.

One issue raised in the discussion of Ethiopia’s inflation is that structural reforms might be a major cause of the surge in food prices, at least between 2004 and 2008. Indirect evidence for an impact of reforms on the formation of cereal prices can be obtained with Chow tests, a structural change should induce a break in the model. Figure 9 accordingly reports one-step, break point and forecast Chow tests for the period 2001:1-2008:11. The Chow tests are far from significant at the 1 percent level, indicated by the straight line at 1.0. Thus, although structural changes in the grain market would need to be assessed more formally with microeconomic surveys, we fail to find evidence of a structural break in our model.

Table 4 reports various diagnostic tests. All diagnostic tests are insignificant at the 5 percent level, indicating that the model is reasonably well specified. Moreover, as the Chow tests in Figure 9 show, it is empirically stable, albeit with some dummy variables. The model explains 85 percent of the variation in cereal inflation as measured by the R<sup>2</sup> , which is quite good given that we use noisy monthly data.

29
- 14 -

interview time of individual household members (e.g. the household questionnaire is filled out in two interviews two weeks apart). The quality of the data collected is enhanced by intensive supervision of all aspects of the survey work, as explained below.

Third, the need for policy relevant data implies that the data must be made available quickly. With this in mind LSMS surveys have pioneered the use of personal computers at all levels of survey operations, from design of questionnaire pages to data entry in the field to analysis of the data. The use of the latest computer technology allows for better quality control (data collected from a household's first interview is entered on personal computers and checked tor internal consistency before the household is .nterviewed again) and rapid data analysis. In the first survey in Cote d'lvoire, a preliminary statistical abstract on the first 6 months of data was available within 2 months of the last interviews in the field. In Jamaica, a report covering issues of health, education, and the impact of food subsidies, food stamp and school feeding programs on the poor was prepared in Kingston in October 1988 using data collected in August 1988, despite a devastating hurricane that occurred in early September of the same year.

Fourth, LSMS surveys are flexible and adaptable to the particular characteristics and policy issues of any given country. The computer-based technology of the LSMS allows for flexibility and speed of implementation for particular countries. The basic questionnaire can easily be supplemented with special modules focusing on specific information needs. Examples of this are the collection of additional health care information (e.g. health facility questionnaire) in the third year of the C6te d'Ivoire survey and supplemental
**Table 4. Distribution of Employees by Education and Firm Size, 2006**

||||**Informal**|**firms**|||**F**|**ormal firm**|**s**||
|---|---|---|---|---|---|---|---|---|---|---|
||**1–5**|**6–10**|**11–50**|**51+**|**Total**|**1–5**|**6–10**|**11–50**|**51+**|**Total**|
|**Incomplete Primary**|||||||||||
|Column|11|9.3|7.01|4.52|9.58|6.79|4.26|3.95|2.58|3.35|
|Row|72.1|12|11.9|3.98|100|11.8|9.14|34.8|44.2|100|
|**Complete Primary**|||||||||||
|Column|23.6|16.4|13.2|8.65|19.8|11|11.1|11.2|10.9|11|
|Row|75.2|10.3|10.9|3.69|100|5.8|7.3|30.2|56.7|100|
|**Incomplete Junior**|||||||||||
|**High**|||||||||||
|Column|6.02|6.08|6.13|3.08|5.79|2.43|3.18|2.92|2.64|2.75|
|Row|65.3|13|17.2|4.48|100|5.15|8.33|31.4|55.1|100|
|**Complete Junior High**|||||||||||
|Column|31|25.8|25|19.8|28.4|22.1|21.8|20.8|24.9|23.3|
|Row|68.6|11.3|14.3|5.86|100|5.52|6.74|26.4|61.3|100|
|**Incomplete Senior**|||||||||||
|**High**|||||||||||
|Column|11.6|12.6|13|14.3|12.2|19.1|18.6|16.1|17.1|17|
|Row|60|12.8|17.3|9.87|100|6.55|7.87|28|57.6|100|
|**Complete Senior High**|||||||||||
|Column|9.27|11.2|11.4|14.5|10.3|17.1|16.1|17.5|14.2|15.5|
|Row|56.7|13.5|17.9|11.9|100|6.45|7.48|33.5|52.6|100|
|**University**|||||||||||
|Column|7.52|18.5|24.3|35.2|13.9|21.4|24.9|27.5|27.7|27.1|
|Row|33.9|16.5|28.3|21.3|100|4.6|6.62|30|58.8|100|
|**Total**|||||||||||
|Column|100|100|100|100|100|100|100|100|100|100|
|Row|62.9|12.4|16.2|8.43|100|5.82|7.2|29.6|57.4|100|

_Source:_ Authors’ computations based on Economic Census and Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo data.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

Three facts are worth highlighting. First, considering the totals for each sector, formal firms are found to be more intensive in educated workers: 42.6 percent of their workforce has at least completed high school, while 14.3 percent have, at most, completed primary school, in contrast to 24.1 percent and 29.4 percent for informal firms, respectively. Second, these patterns hold controlling for firm size. Thus, for instance, in informal firms with up to five workers, an average 14
its weight in the construction of the composite index. The blue dot in figure 6 labeled ‘power’ shows that entrepreneurs in states with lower levels of private domestic investment complain more about cost of power than those in states with higher levels of domestic investments<sup>21</sup> . Similarly the red dot labeled ‘transportation’ shows that transport appears more of a constraint to businesses in states that show lower levels of growth compared to fast growing states. Both these indicators present the highest values on the y-axis, meaning that they are the most important indicators in the construction of the overall ICI. On the contrary figure 7 shows that the most important institution bottlenecks in the construction of the ICI, such as corruption, crime and tax regulations, are all considered equally binding by managers in both slow growth as well as low investment states.

# **4. Conclusions**

In closing, this paper has shown that by building a composite indicator of the investment climate in 16 Indian states, it is possible to identify an order of priority among the most important bottlenecks to a better business climate in India. The analysis of 46 investment climate variables shows that power, transportation, corruption, tax regulations and theft remain the major bottlenecks policymakers need to address in order to improve the business environment in India. This conclusion is confirmed by evidence that infrastructure is particularly binding to states that present a low level of investment and growth. On the contrary, institutions appear to impact high growth (or investment) states as well as low growth (or investment) almost equally.

> 21 Figure 6 shows on the x-axis the difference in the level of each constrain between low investment (or low growth) and high investment (or high growth) states, and on the y-axis the importance of the indicators in the construction of the ICI (represented by the share of variance explained by each factor). Consequently values at the top right of the figure represent business environment indicators that have the highest weight in the ICI and for which each category of states performs differently than the other.

17
Policy Research Working Paper 6088

# **Abstract**

This paper summarizes financial inclusion across Africa. First, it provides a brief overview of the African financial sector landscape. Second, it uses the Global Financial Inclusion Indicators (Global Findex) database to characterize adults in Africa that use formal and informal financial services and identify the barriers to formal account ownership. Next, it uses World Bank Enterprise Survey data to examine how the use of financial services by small and medium enterprises in Africa compares with small and medium enterprises in other developing regions in terms of account ownership and availability of lines of credit. The authors find that less than a quarter of adults in Africa have an account with a formal financial institution and that many adults in Africa use informal methods to save and borrow. Similarly, the majority of small and medium enterprises in Africa are unbanked and access to finance is a major obstacle. Compared with other developing economies, high-growth small and medium enterprises in Africa are less likely to use formal financing, which suggests formal financial systems are not serving the needs of enterprises with growth opportunities.

This paper is a product of the Finance and Private Sector 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 ademirguckunt@worldbank.org and lklapper@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
factors it loads most heavily on, and infer which of these two major types of corruption it is best capturing.

When NIT is added in the factor analysis, it has a large positive loading on the administrative corruption factor, but a small negative loading on the state capture factor. Very similar results are found if any of the other broadly-defined corruption indicators listed across the columns of table 8 is substituted for NIT.

Factor analyses based on the 9 WEF variables in Table 8 produce similar findings to those based on BEEPS variables. Two significant factors explain 95% of the variation. These factors are again clearly identifiable as state capture and administrative corruption, with the first four WEF variables listed in Table 8 loading most heavily on one factor, and the other five loading most heavily on the other. Corruption measures from NIT, CPIA, TI and WBI all load mostly on the administrative corruption factor when any one of them is added as a 10<sup>th</sup> variable. The ICRG, EIU and the “obstacle” measure from BEEPS load more equally across the two factors, although still somewhat more strongly on administrative corruption than on state capture.

The weak link between state capture measures in BEEPS and in broader perceptions-based indicators is due in part to Belarus and Uzbekistan. These two countries are “outliers” in being rated lower by other sources than by most of the BEEPS questions, particularly those on state capture. The first _Anticorruption in Transition_ report (World Bank 2000) attributed low levels of state capture in Belarus and Uzbekistan to their relatively small private sectors and “the continued existence of authoritarian controls.” The third report (Gray and Anderson, 2006) discusses in greater detail the possibility that corruption takes different forms, not easily measured by firm 43
Policy Research Working Paper 7680

# **Abstract**

Front-line loan officers of microfinance institutions are important in acquiring information on potential borrowers and selecting them in accordance with the microfinance institution’s mission. This study uses a unique data set on loan officers and their loan portfolios from China’s largest nongovernmental organization microfinance institution to test whether officers’ personal characteristics affect the size and quality of their loans. The analysis uses a period in which the institution shifted from reliance on government donations and subsidies to commercial sources of funding. Imposing more commercial incentives on loan officers could affect how they balance potentially competing objectives to serve the poor and pursue profitability. The paper finds that loan officers who were formerly farmers or worked in local government were better able to maintain lending to poorer borrowers, without incurring substantially lower repayment rates on their loans. In short, it appears that the career backgrounds of loan officers did play a role in preventing mission drift.

This paper is a product of the Finance and Private Sector 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 rcull@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 2a: Distribution of financial literacy across demographics**

|||Inte|rest|infla|tion|ri|sk|Ov|erall|
|---|---|---|---|---|---|---|---|---|---|
||% Sample||d‘||d‘||d‘||d‘|
||<br>size<br>(total: 1,366)|correct<br>(%)|ont<br>know<br>(%)|correct<br>(%)|ont<br>know<br>(%)|Correct<br>(%)|ont<br>know<br>(%)|3 correct<br>(%)|ont<br>know<br>(%)|
|||<br>|<br>|<br>_Age _<br>|<br>|<br>|<br>|<br>|<br>|
|35 and younger|32.28%|47.39%|18.82%|56.24%|18.59%|19.27%|28.80%|5.44%|42.40%|
|36 to 50|27.67%|42.59%|20.63%|52.65%|20.11%|13.23%|26.72%|2.38%|43.65%|
|51 to 65|23.28%|29.87%|39.94%|53.14%|29.25%|9.75%|35.22%|2.20%|59.12%|
|Older than 65|16.76%|13.54%|70.31%|34.06%|46.29%|3.93%|62.88%|0.87%|84.72%|
|||||_Gender_||||||
|Male|42.17%|36.81%|28.99%|52.43%|21.88%|14.41%|29.86%|3.82%|47.22%|
|Female|57.83%|35.95%|35.70%|49.62%|29.24%|11.65%|39.49%|2.53%|58.48%|
|||||_Education_<br>||||||
|Less than HS|8.42%|19.13%|62.61%|35.65%|39.13%|8.70%|58.26%|1.74%|77.39%|
|High school|31.55%|35.27%|34.57%|49.42%|27.84%|12.99%|32.95%|3.71%|54.99%|
|Technical|37.26%|34.18%|32.02%|51.28%|26.33%|12.57%|36.54%|1.96%|54.81%|
|Some college|5.34%|53.42%|23.29%|49.32%|24.66%|12.33%|32.88%|4.11%|47.95%|
|Higher education|17.42%|45.80%|20.17%|60.08%|16.81%|15.13%|27.31%|4.62%|39.50%|
|||_Se_|_lf-employed,_|_non-employ_|_ed, and wor_|_kers_||||
|Self-employed|2.64%|30.56%|30.56%|47.22%|19.44%|16.67%|25.00%|0.00%|47.22%|
|Workers|53.73%|41.42%|24.11%|52.59%|20.84%|14.17%|28.75%|3.27%|45.64%|
|Non-employed|18.67%|42.35%|27.45%|55.29%|26.67%|14.90%|31.37%|3.14%|51.76%|
|Retired|24.96%|21.41%|56.01%|43.99%|37.83%|7.92%|53.96%|2.93%|73.31%|

9