rafmacalaba/data-use-mentions-extended huggingface dataset:
consumption.

The National Urban Employment Survey ( _Encuesta Nacional de Empleo Urbano_ , ENEU) is also a microleveled data set collected by INEGI. It contains quarterly wage and employment data of the last twelve years (1987-1999). Currently, the data is representative of the 41 largest urban areas in Mexico. It covers 61 percent of the urban population (2500 inhabitants or more) and 92 percent of the metropolitan population (100,000 or more inhabitants).

The data is from household surveys, which fully describe family composition, human-capital acquisition and experience in the labor market. The variables contain information about social household characteristics, activity condition, position in occupation, unemployment, main occupation, hours-worked, earnings, benefits, secondary occupation, and job search. The sampling design was stratified into several stages (where the final selection unit is the household) with proportional probability to size. This statistical construction allows us to compare different years.

# **4. TEACHER’S PROFILE WITH RESPECT TO OTHER OCCUPATIONAL GROUPS—A DESCRIPTIVE ANALYSIS**

# **_Definitions_**

“Teacher” refers to all individuals whose main occupation is public or private instruction. A combination of descriptive statistics is used to examine the income structure and professional profile of basic school teachers with respect to other occupational groups. In this paper, teachers were divided by the level they taught by urban-rural location, and by public-private school status. Following other authors, several occupational groups were chosen in order to provide a yardstick for comparing teachers’ salary structure and professional profile.

From the ENIGH survey, occupational groups included people employed in agriculture, fishing and forestry ( _the agricultural_ group), and people employed in low-skilled activities such as street vendors and servants ( _the low-skilled_ group). The _mixed-skilled_ group includes professionals; technicians; artists, and sportsmen; managers and directors in the public as well as in the private sector; managers and workers in the

> 5 Federal, State plus Autonomous schools teachers.

6
.

We develop a theoretical model that links the equilibrium allocation of inspection resources to the underlying probabilities of non-compliance and to ambiguity surrounding those compliance with import regulations.

> 2During field work in a middle-income country, one of the authors visited a border station in which an agency tasked with inspecting imports for sanitary risks stored its inspection records in paper form. The documents from several prior years were kept in large canvas bags piled together in the corner of a room. It is unlikely that these records were at all useful for informing subsequent inspection decisions in real time. The information that was retained may well have resided only in the minds of individual inspectors.

2
has shown that a reasonable de minimis threshold can be vital for small business firms that depend on cross-border sales.<sup>86</sup> Among the 21 countries covered under Digital Business Indicators project, Bangladesh, Burkina Faso, Pakistan, Senegal, and Tunisia do not have a de minimis threshold in place. Thresholds are often set based on the value of the consignment, ranging from zero up to €1,000 (figure 10). Kenya and Tanzania set a low consignment value threshold of less than US$5. In member countries of the Eurasian Customs Union (ECU), the threshold is also based on the weight of the consignment.<sup>87</sup>

**Figure 10. Countries set different levels of a de minimis threshold for customs procedures**

22
can be used.

The quantitative data confirms that while women are more likely than men to make the decisions about the water source for drinking, the decisions are often made jointly. But it varies by household structure. When the husband has more than one wife, he may not be in the home often. As such, a wife is delegated more of the decision-making responsibility. In extended family households, the daughter-in-law is often charged with fetching water for the household. The mother-in-law does not fetch the water typically but may have other chores and she may participate in decisions on water expenditures. Young women respondents explain that the mother-in-law’s involvement depends on the relationship: “... _in this dry season and you use the water communally, your mother-in-law could help, and if she can’t get there [she] might send a motorbike to deliver water. You can help each other. It’s dependent on how you relate.... she can use her money and send motorbikes to deliver the water_ .” Compared to men, women are less likely to be directly involved in individual-level (or personal) decisions such as health care for self, visiting family, and the use of own income. Even so, there is a significant share of men (31 percent) who also do not directly participate in at least one individual-level decision. There are 11 percent of men who do not directly participate in two or more individual-level decisions. These are primarily young men who have not yet started their own household.

Figure 1 presents the distribution of activities for which respondents are not directly involved in the decision, by number of activities in which they are not directly involved. The observed patterns are in line with those discussed thus far. When women are not directly involved in decisions, these decisions tend to regard household expenditures, followed by personal decisions. When men are not involved in decisions, the decisions most commonly regard water allocation and use, and the water source.

7
# **1. Introduction**

While there is a great deal of uncertainty over the exact magnitudes of the global changes in temperature and precipitation, it is widely accepted that significant deviations of the variability of climate from its historical patterns are likely to occur (IPCC, 2007).<sup>3</sup> Considering that millions of poor households in rural areas all over the world are dependent on agriculture, there are increasing concerns that the change in the patterns of climatic variability is likely to add to the already high vulnerability of households in rural areas of developing countries, thus posing a serious challenge to development efforts all over the world. In view of this impending threat of climate change upon the poor, it is critical to have a deeper understanding of the household _adaptation_ strategies and targeted measures that could mitigate the poverty impacts of erratic weather. With these considerations in mind, in this paper, we carry out an analysis of the welfare impacts of climatic variability in the rural areas of Mexico.

Our objectives are threefold. First, we try to quantify the extent to which unusual or erratic weather has any negative impacts on the welfare of households. Based on historical experience and the multiplicity of economic and institutional constraints faced, rural households in Mexico, as most rural households all over the world, have managed to develop traditional strategies for managing climatic risk. Eakin (2000), for example, documents how smallholder farmers have adapted to climatic risk in the Tlaxcala region of Mexico. Yet, quantitative evidence on how successful such risk management strategies are at protecting household welfare in Tlaxcala or elsewhere in Mexico is quite scarce.<sup>4</sup> To the extent that the current risk-coping mechanisms are not very effective in protecting welfare from erratic weather patterns one can be quite certain that the change in the patterns of climatic variability associated with climate change is likely to reduce the effectiveness of the current coping mechanisms even more and thus increase household vulnerability further. We use two separate nationally representative household surveys
360)|(0.844)|(0.311)|(0.312)|
|Observations||17,736|17,167|17,736|17,726|17,726|
|R-squared||0.008|0.004|0.000|0.005|0.006|
||**D. **|**Combined m**|**easures**||||
|||(17)|(18)|(19)|(20)|(21)|
|Withheld from randomization (WFR)||0.079***|1.015***|0.177|-0.110***|0.114***|
|||(0.009)|(0.234)|(0.139)|(0.032)|(0.033)|
|Excess interaction share||0.093|1.196|0.147|0.032|-0.032|
|||(0.094)|(1.609)|(1.251)|(0.104)|(0.098)|
|WFR*Excess interaction share||0.051|1.169|-0.386|-1.286***|1.262***|
|||(0.067)|(2.000)|(1.531)|(0.327)|(0.325)|
|Observations||17,736|17,167|17,736|17,726|17,726|
|R-squared||0.027|0.011|0.001|0.015|0.016|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses
Policy Research Working Paper 9938

# **Abstract**

Tournament competition is viewed as motivating bureaucrats in promoting growth. This paper examines how this incentive leads to economic performance manipulation. Using data from Chinese cities, the analysis shows that performance exaggeration increases over the course of the first term of the top bureaucrat, peaking in the last year of his or her term. Winning a tournament competition is behind this performance manipulation: political rivals reinforce each other in exaggerating performance, and political competition intensifies the tendency for manipulation. Performance exaggeration leads to higher chances of promotion, but the ratchet effect (that is, better performance today leading to a higher target tomorrow) and the potential to blame predecessors induce restraint. A good local institutional environment also restrains performance manipulation.

This paper is a product of the Development Research Group, Development Economics. 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 lxu1@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
where _i_ represents each household and _g_ the growth observed in per capita household final consumption expenditure reported in World Development Indicators.<sup>3</sup> For details on the interpolation methodology, see Prydz et al. (2019). We then collapse the 1 and C 20201 consumption vectors into 1,000 equally weighted quantiles in each distribution and estimate the growth for each quantile. Finally, we use FFCC2019 these quantile growth rates to grow the distribution (2019 FY distribution for India used in PIP, which his based on approach 2) to derive the C 2020c distribution for India. This ppiipp FFCC2019 paper reports estimates of inequality and poverty based on the C 2019 and C 2020c cciicc change in India. Our approach makes two assumptions: First, we need to interpolate to get from ppiipp cciicc fiscal years to calendar years. This is a standard PIP assumption whenever data do not line up with calendar years. Second, we assume that the growth rates estimated using approach 1 can be applied to the approach 2 distribution (using 1,000 quantiles). This seems to be a reasonable assumption given that Roy and van der Weide (2022) show that despite differences in poverty levels, the trend in the poverty rate is very similar across the two approaches. Once estimates from approach 2 become available, this assumption can be tested, and our estimates can be updated.

Table A.1 reports the 2020 estimates of poverty using various growth rates for 2020. Using the distribution-neutral per capita GDP growth from national accounts results in an extreme poverty rate of 11% which would mean a global poverty headcount rate of 8.8%.<sup>4</sup> Using unadjusted growth rates from the CPHS (as published by Gupta et al., 2021) instead would result in a poverty rate of 16.2% in India implying a poverty rate of 9.8% globally amounting to a net impact of 130 million additional poor. The preferred approach in this paper also employs the
generally less resilient and more vulnerable. On the other hand, asset-rich households or those with savings might be less affected by climate shocks because they can liquidate their assets and/or use savings to smooth consumption and avert food and income insecurity. To achieve a more nuanced understanding of how climate shocks impact livelihoods, it is important to classify households into sub-groups that differ along dimensions that inform their vulnerability and resilience.

Climate shocks could increase household vulnerability and reduce resilience through various pathways. Weather and climate variability affect rural household consumption and incomes, more so for those who depend on rainfed agriculture for their livelihoods. In the event of disruptions to agricultural production, poor households are likely to intensify extraction of natural resources and to engage in non-farm employment (Mulenga et al., 2014; Angelsen and Dokken, 2018). Increased natural resources extraction, such as charcoal production and logging, speed environmental degradation, which disproportionately affects smallholder farmers. Moreover, intensification of environmentally degrading livelihood activities can aggravate poverty for households whose livelihoods depend on these natural resources.

Households with a higher asset base could liquidate some to help smooth income and consumption, but this is only a temporary coping strategy. If climate or weather shocks persist for a longer period, households may deplete their assets and savings, causing a further slide into poverty. Absent formal insurance or social protection, this creates a vicious cycle in which climate shocks increase poverty and environmental degradation that create more climate shocks. Households my also use incomes earned from non-farm employment to smooth consumption in places where such work is available. The outcome will either be vulnerability or resilience, depending on whether (i) a household stays or becomes poor, or (ii) was never poor or escaped poverty.

# 4. Data and methods

## 4.1 Data sources

We draw data from the three-wave, nationally representative RALS survey conducted by the Indaba Agricultural Policy Research Institute (IAPRI) in collaboration with the Ministry of Agriculture and the Central Statistical Office (CSO) of Zambia. The first
# **Figure 5. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH wo/children FHH w/children Non-FHH<br>40 60<br>55<br>35<br>50<br>30<br>45<br>Percentage (%) Percentage (%)<br>25 40<br>60 50<br>55 45<br>50<br>40<br>Percentage (%) 45 Percentage (%)<br>35<br>30<br>45<br>40 25<br>35<br>20<br>30<br>Percentage (%) Percentage (%)<br>25 15<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

39
# **3. An expository model**

An important point that has not received adequate attention in the literature on antipoverty policies is that heterogeneity in individual economic welfare at any given level of aggregate household welfare can restrict the scope for reaching vulnerable women and children using household poverty data. And this is the case even when there is a strong household income effect on individual welfare. To anticipate our empirical work, we shall identify individual welfare by nutritional status.

We elaborate this point in a simple expository model. The nutritional attainments of an individual (adult woman or child) are denoted _n_ , while the wealth of the household to which that individual belongs is _w_ . To keep notation simple, we can assume that _n_ and _w_ are both normalized by appropriate cut-off points (stipulated nutritional thresholds or poverty lines) such that a person is undernourished if (and only if) _n_ <1 and a household is poor if _w_ <1. These two random variables have a (continuous) joint density _f_ ( _n_ , _w_ ) .

As discussed in the prior section, the relationship between the two variables depends on a number of factors, including intra-household inequality, the local health environment (including water and sanitation), access to relevant health and nutritional knowledge, and child care. To keep our expository model simple, we collapse the heterogeneity into one composite factor denoted  , which we can take to be scaled such that it is bounded below by zero and above by unity. For concreteness, we might suppose that  is the share of the household’s total nutritional intake devoted to other household members. The expected value of individual nutritional status given _w_ and  is: It is assumed that the function _n_ (.) is strictly increasing in _w_ —the slope of this function with respect to _w_ is the aforementioned wealth effect on undernutrition—and that the function is strictly decreasing in  at given _w_ . (Continuing the previous example, we can have the special case _n_ (.) 
to abductions. When restricting the sample to women who have not moved since 1996, the coefficients for both conflict variables become larger, the effect of the casualties variable becomes significant, but is smaller than in Column (2), which suggests that some of the effect captured by casualties in Column (2) is due to the correlation of casualties with abductions. The results in Column (4) indicate that moving from the 5<sup>th</sup> to the 75<sup>th</sup> percentile of the casualties (abductions) distribution leads to an increase of 7.1 (2.7) percentage points in the probability of being married by age 15. Taking these results back to the findings on education, it is interesting to remark that, although overall conflict intensity appears to have had a small positive effect on female schooling attainment, it has tended to increase the probability of early marriage. This may explain why female enrollment has not increased despite the increase in attainment.

# Table 9 goes about here

Table 9 presents findings on the impact of conflict during marriageable age. All columns except Column (3) refer to regressions of an indicator variable equal to one if the woman is married by the age of 21 and zero otherwise, using data from the 2006 DHS. Column (3) exploits the richer migration data available in the NLSS 2003/2004, and focuses on the same treated and control cohorts as in the 2006 DHS analysis. In order to account for the fact that the youngest cohort is observed three years before the age at which they are surveyed in the 2006 DHS, in this specification I consider the effect of conflict on the probability of being married by the age of 18. Women whose entry in the marriage market coincided with the start of the conflict are no more or less likely to be married by the age of 21, when more than 80 percent of Nepalese women are married (Figure 1). No significant difference is noticeable already by
--|---|---|
||Number of<br>People|Percent of<br>All<br>Tourists|Total<br>Expenditures,<br>US$’000|US$/Trip|Percent of all<br>Tourists|US$/Trip|
|Recreation|569,323|24%|409,947|720|63%|509|
|Business|309,993|13%|151,859|490|21%|767|
|Transit (indirect)|312,004|13%|54,598|175|6%|226|
|Transit (direct)|1,140,430|48%|313,481|275|na|na|
|Other|54,160|2%|30,182|557|10%|536|
|All tourists|2,385,910||960,067|402|100%|549|

_Note:_ Expenditures in the national accounts and the survey data in this table exclude the cost of international travel by air and land.

_Source:_ Authors’ estimates. National accounts data for 2006 are based on the extrapolation of historic trends (not survey-based); TSA data from tourism surveys (2007–08).

Data on tourists’ expenditures from TSA surveys, which are based on a sample of 1,626 foreign tourists, are scaled up to the national level, taking the total number of foreign tourists—1,245,480 tourists excluding direct transit visitors—as given (Table 3). On aggregate, the largest single expenditure items are lodging and hotel expenses and personal shopping, apart from the cost of international travel (Appendix Table A6). Total expenditures for foreign tourists from the survey data are allocated to each province using the information on the share of time (the number of days) each tourist spent at each destination.<sup>15
2

# **A. The Growth of Education Attainment**<sup>**1**</sup>

Education attainment levels increased rapidly in most developing countries since the 1950s; Schultz (1988). While Mexico also partook in that development, earlier studies had identified a significant lag in its education indicators. Londonto (1996), for example, points to an "education deficit", according to which the Latin American countries in general, and Mexico in particular, have approximately two years less of education than would be expected for their level of development (as measured by per-capita incomes). Elias (1992) finds that education was the most important source of labor quality improvement in Latin America between 1950 and 1970, but points out that such improvements did not take place to the same extent in Mexico. This changed dramatically in the 1980s, as shown in the scatter diagram below.

Figure 1 describes the relation between income per capita and average years of schooling of the population aged 15 years or more, using stacked cross-country data for the years 1960, 1970, 1980, 1985 and 1990.2 Mexico's level of education

- 'The following discussion primarily focuses on the evolution of education attainment levels as measured by years of schooling, on the assumption that the quality of education has remained more or less constant. Although it is of great interest in this context, information on the quality of education in Mexico is very scarce. The most common measures of "quality" are based on input measures, (e.g., real expenditures on education per student, student-teacher ratios, or class-density variables) or secondary performance variables (e.g., evolution of repetition and desertion rates). On the basis of these measures, Mexico also has performed comparatively well over the last few decades. These measures are not very satisfactory, however, as they reflect many other influences in addition to the quality of instruction as reflected in the acquisition of knowledge and skills. A comprehensive