rafmacalaba/data-use-mentions-extended huggingface dataset:
ALT):_** _Face-to-face survey at the end of the masika, alternate survey module_

- For recall ALT households, a face-to-face end line survey was conducted after the harvest (July–September 2014), during which both labor and farm production information was collected. For each plot, the household members who had worked on that plot at any point during the season were identified, and the following information was reported: (a) total weeks worked on the plot over the season (irrespective of activity), (b) approximate number of days per week worked, and (c) approximate number of hours worked per day.

Throughout this paper, we establish the magnitude of bias through comparisons with the weekly visit design. This is based on the premise that the data reported in the weekly visit design are likely to be the closest to the actual situation given that weekly visit and weekly phone respondents were probed for day-by-day responses that were specific to the plot and to the person. Accordingly, these interviews minimized the need for respondents to make any complicated calculations or inferences regarding seasonwide labor. While we cannot exclude the possibility that forgetting or telescoping still led to some bias, we assume that the short one-week period and

> 9 In addition, after the hours per person per day over the previous week were reported, the range of activities performed during that time was recorded (land preparation and planting; weeding; ridging, fertilizer application, and other nonharvest activities; harvesting), but the number of hours were not specified for each activity.

> 10 The weekly phone interview design draws on lessons summarized by Dillon (2012), who uses a phone survey to collect information on purchased input applications among cotton farmers in Tanzania. Similar recent work has used phone surveys to collect high-frequency data on economic activity. See Garlick, Orkin, and Quinn (2015) for a review of the literature on phone-based strategies for collecting household and enterprise data.

8
flows. Table 3 shows that the same results hold for the correlation of CPI inflation.

Table 4 comes back to the use of gross trade indicators but this time constructed using data from Borin and Mancini (2019) based on EORA data. The goal of separating the (gross) export flows that are directly absorbed from the share that is re-exported further. Results in column (1) show that the correlation of CPI inflation increases when two countries trade more intermediate inputs, which confirms the insight from 15
financing other than bank loans; and (iii) the quality of the enforcement of bank capital rules.

# Sub-national **Borrowing in Mexico: The Initial Position**

Sub-national debt in Mexico has so far not been a national macroeconomic problem (as it has in Brazil for example), but it could rapidly become one. In 1997, the recorded stock of state and municipal debt was equivalent to about 2 percent of Mexico's GDP (and about 6 percent of total public debt).<sup>3</sup> By comparison, sub-national debt amounts to 5 percent of GDP in Argentina, almost 20 percent in Canada, and somewhat above that level in Brazil. In addition, the Mexican constitution prohibits states from borrowing in foreign exchange or from foreign creditors.

Behind Mexico's relatively comfortable sub-national debt statistics, however, lie three potential destabilizing factors-moral hazard, limited actual servicing capacity, and contingent liabilities. Prior to the 1990s the federal government, through the governing party's infrastructure, had control over the state governments, and thus ultimately over any state borrowing decisions Over the course of the 1990s, rapid democratization ended much of this federal political control, which allowed states to take advantage of the federal government's concern for both the banking system (which, following its 1995 crisis, had

> 3. Most Mexican states have much less disposable income to service debt than usual accounting would suggest, since much of their current expenditures is "tied" to _defacto_ inflexible commitments (notably salaries). The ratio of debt stock to disposable revenue, the latter defined as own taxes plus untied transfers, ranges from a maximum of 1.8 (in Sonora) to a minimum of 0.02 (in Hidalgo). By that measure, the eight most indebted states are Sonora, Nuevo Le6n, Mdxico, Queretaro, Quintana Roo, Baja California Sur, Jalisco, and Sinaloa, all with a ratio of debt stock to disposable revenue greater than 1 (Hemandez and Oliveira,
With an increase in the frequency of unforeseen extreme weather events that threaten seasonal crop production and thereby rural livelihoods (McCarthy et al., 2017), cassava cultivation is garnering more attention, as also evident in the National Agricultural Policy (MoAIWD, 2016). The promotion of cassava production and value addition in Malawi requires a change in the approach to public spending on agriculture, of which an average of 50 percent was geared towards maize production during the period of 2006-2013 (FAO, 2014). To inform such process, one needs reliable time series data on cassava cultivated area, production and yields. Currently, there are at least two sources of annual, national-level cassava statistics in Malawi.P6F<sup>7</sup> Agricultural Production Estimates Survey (APES), which is conducted annually by the Ministry of Agriculture, Irrigation and Water Development (MoAIWD), is the first source. APES approach to cassava yield estimation is based on crop cutting, although the operational documentation on the APES cassava crop cuts, specifically their supervision, timing and processing, is not available. The latest publicly available APES report for the 2015/16 season reports a national cassava yield of 17,564 kilograms per hectare (kg/ha). The same report puts the figure for the 2014/15 season at 18,042 kg/ha.P7F<sup>8</sup> FAOSTAT is the second source of cassava statistics in Malawi. Figure 1 shows the FAOSTAT national yield and harvested area estimates from 2005 to 2014 (i.e. the latest year for which the estimates are available). The exact details on the computation of the FAOSTAT-based statistics are not available, and they are presumably at least in part a function of the information provided by the MoAIWD in response to the annual FAOSTAT questionnaire. If true, the increase in the national cassava yield from 14,300 kg/ha in 2005 to 22,504 kg/ha in 2014, as well as the parallel surge in the
<sup>29</sup> The implementation of the proxy means test within HIECS was calibrated so that each governorate represented in HIECS absorbs the same number of beneficiaries and benefits as confirmed in administrative totals.<sup>30</sup>

# **Tamween program**

Egypt’s food ration program delivers to each beneficiary household a cash credit in the amount of LE 15 per person per month that can be used at family stores for the purchase of 21 basic food necessities. Food ration beneficiary status in the HIECS 2015 is directly observed through the module that records purchases using the food ration card made by households: any household purchasing any positive amount of any good available for purchase with the food ration card is assumed to receive a transfer in the amount of LE 15 per household member per month.

The food benefit received by the households is expanded to include an estimate of the value of the Baladi bread (and bread points for unconsumed bread) that the household receives. From the expenditure data in the HIECS 2015, we use a 5 piaster per loaf transformation to estimate the number of loaves of Baladi

> 28 The estimate was obtained from discussions with the Ministry of Finance. There are no other data available that could allow estimating a basic wage share of total wages at further disaggregated levels.

> 29 Following the programs’ application form, the predictive eligibility model in the HIECS used the following predictors of household consumption: number of members, number of rooms in the residence and dwelling characteristics (access to water, sanitation, construction materials, and so forth); ownership of food smart card; access to electricity; and ownership of assets (cars, smartphone, internet access, refrigerator, and so forth). 30 Takaful and Karama Program Progress Summary June 17, 2017.

23
critical to aligning the incentives of lower level bureaucrats with the promotion of local economic development ( _c.f._ Landry 2008).

# **VI. CONCLUSIONS AND FUTURE RESEARCH**

This paper examined how the investment decisions of firms in different regions of the Russian Federation are shaped by informal practices linked to regulatory enforcement and their consistency. Specifically, we examined how levels of corruption compare to uncertainty about the level of corruption and inconsistency in property rights enforcement. Our hypotheses on levels of corruption were straightforward and we argued that in a highly regulated environment like Russia, high regional average levels of corruption should create conditions which depress investment. That is, corruption enables high levels of regulation to impose heavy transaction costs on firms. Our hypotheses on uncertainty instead stem from the preposition that in imperfect institutional environment – where laws are explicitly being broken or applied selectively – firms must inevitably adjust to these informal rules. Part of this adjustment involves adopting expectations about how these informal practices will be carried out and growing comfortable with making investment decisions influenced by these expectations. As with the more formal regulatory environment, however, firms must also adjust their expected returns on investment based on expectations about shifts in these informal institutions. The greater uncertainty in how informal practices will function, the less likely investment. In this paper, we explored two types of uncertainty that can emerge in imperfect institutional settings: administrative corruption in the absence of centralizing authorities and inconsistency in property rights enforcement as exemplified by violent “raider” attacks abetted by the authorities.

To test our hypotheses, we combined data from several recent sources. Information about firms’ investment decisions (and other firm-level indicators) was extracted from the 2014 RuFIGE survey; data on corruption levels and variation within regions was taken from the 2012 RR BEEPS; and finally, data on raider’s attacks was taken from complaints about raider’s activity filed with the Center “Business Against Corruption”.

Our regression analysis suggests that levels of corruption were not an important factor in investment decision,
examined in Model 1, and all interaction terms with child gender were subsequently included in Model 2.

Interactions from the fully interacted model were further examined by calculating simple slopes and plotting marginal means of child development scores for boys and girls at varying levels of the risk/protective factor (i.e., at values of 0 or 1 for dichotomous variables and from -1 to 1 standard deviations below and above the mean for standardized continuous variables).

# **Results**

## **Descriptive statistics and bivariate associations of key variables with child gender**

Table 1 summarizes the descriptive statistics and bivariate associations in order to understand how the distribution of key risk and protective factors and relevant demographics varied by child gender for children aged 0-35 months and 36-72 months. Estimated ECE enrollment in our sample was higher (35%) than estimates from the 2019-2020 Pakistan Social and Living Standards Measurement (PSLM) (19%). The estimate of the percentage of the population residing in rural areas aligns with findings from other national surveys (United Nations Population Division's World Urbanization Prospects, 2018). Distributions of child development outcomes and risk and protective factors showed no significant differences by child gender for either age group. Initially, unadjusted results indicated a higher proportion of male caregiver respondents for boy children (84%) compared to girl children (79%) in the older age group. However, this difference was not statistically significant after adjusting for multiple hypothesis testing. Overall, in the sample, a majority of caregiver respondents were male (78%). Additionally, in both age groups, the average age of girls was slightly younger (~1 month) compared to boys, and households with girls had a marginally lower mean monthly expenditure on mobile phone compared to those with boys, though these differences were not statistically significant.

## **Associations between risk/protective factors and child development, and variation by child gender**

Appendix Table I presents results from multivariate regression analyses where each
By merging these sources, we create an individual-level dataset for Brazil for students who entered our surveyed programs in 2014. It includes student gender, age, and mother’s education level; graduation status as of 2016; and pre- and post-program labor market outcomes for graduates who were formally employed after graduation. We merge the individual-level dataset with information at the program- and HEI- levels from the WBSCPS and other administrative data. The resulting dataset includes 29,453 students and 401 programs (relative to 601 surveyed programs).<sup>17</sup>

# **Ecuador**

**_2019 Higher Education Census (HEC)._** This is the universe of higher education graduates who obtained their degrees between January and December of 2019. It comes from the Science, Technology, and Innovation Secretariat ( _Secretaria Nacional de Ciencia, Tecnologia e Innovacion_ , SENESCYT), and contains information on approximately 29,000 SCP graduates, including field of study, institution, program name, and graduation date. Based on these data, we define a student’s peers as those who also graduated from her program in 2019.

**_National Educational Entrance Examination._** This dataset comes from the National Institute

for Educational Assessment ( _Instituto Nacional de Evaluacion,_ INEVAL) at the Ministry of Education. It records test scores on the mandatory high school exit exam ( _Ser Bachiller_ ) and selfreported student socioeconomic background at the time of the exam. We obtained access to a subset of this dataset through the Higher Education Access Unit ( _Subsecretaria de Acceso a la Educacion Superior_ ) at SENESCYT, with information on students who took the test in 2017 and

> 17 Some programs do not match because they did not yet exist in 2014, which is our cohort’s entry year. Others did exist in 2014 but did not have graduates who were formally employed during our sample period. Table A8 presents descriptive statistics for the subsample of 401 programs matched to individual-level data. It also shows t-tests for
GDP PC), Province, Sector,|Yes|Yes|Yes|
|Year||||
|Observations|3,649|10,335|3,415|
|R-squared|0.087|0.098|0.094|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

45
" and Mogridge, "The Prediction of Car Ownership." Tanner predicted a saturation level of 450 cars, and Mogridge, 660, per thousand persons for the UK.

26 Ingram and Liu, "Vehicles, Roads, and Road Use." 27 This section draws heavily from Ingram and Liu, "Motorization and the Provision of Roads," and "Vehicles, Roads, and Road Use." 28 Esra Bennathan, Julia Fraser, and Louis Thompson, "What Determines Demand for Freight Transport?" Policy Research Working Paper 998 (Washington, D.C.: World Bank, 1992); David Canning, "A Database of World Infrastructure Stocks 1950-1995" Policy Research Working Paper 1929 (Washington, D.C.: World Bank, 1998); and Ingram and Liu, "Motorization and the Provision of Roads," and "Vehicles, Roads, and Road Use." Bennathan and others used data from 36 countries to analyze the relation of domestic rail and road freight transport demand (in ton-kilometers) to country income and land area variables. Canning analyzed several infrastructure stocks--including roads, telephones, and electric generating capacity--using a panel data set containing from 95 to 145 countries. Ingram and Liu analyzed the regularities of provision of roadway length using data from 50 countries and 35 to 37 cities spanning a wide range of income levels; their findings are summarized in this section.
11 Data availability constrains us to assume that the input cost shares of manufacturing sectors remain relatively stable around each data point we have available. Then, we match these shares in 1995 with the restrictiveness indicator in 1997, the shares in 2000 with the restrictiveness indicators in 2003, and the shares in 2005 with the restrictiveness indicators for 2006 and 2010. Given that we do not have data on manufacturing TFP beyond 2010, we do not use the available OECD indicators for 2011 and 2012. In any case, there are no changes apparent in those two periods with respect to the previous scores. 12 Our approach follows that of Arnold, Javorcik and Mattoo (2011).

> 13 TFP is measured using a multilateral index as suggested by Aw, Chen and Roberts (2001).

11
but it is unclear how or whether she accounts for pre-estimation of the electricity variable in this regression).

> 38 Employment in South Africa is determined by a census question on whether a person (age 15 – 59) works, including formal work for salary or wage, informal work such as making things for sale or rendering a service, or farm work.

39 Dinkelman (forthcoming) uses gradient of the land as an instrument for electrification, and includes as covariates 1996 household density; fraction of households living below a poverty line; distances to the grid, road and town; fraction of adults that are white or Indian to proxy for local employers; fraction of men and women with a completed high school certificate; and two standard proxies for community poverty, the share of female-headed households and the female/male sex ratio, plus district fixed effects.

40 Grogan and Sadanand (2011) use municipal population density in 1971 and the interaction of population density with mean slope gradient of municipal land as instruments for household electricity connection in 2005, and include multiple controls at municipal and household level (e.g., capturing characteristics of labor market and general infrastructure).

35
**Notes:** PPML HDFE estimates using annual pair-wise total trade data from COMTRADE/Baci. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Note that one must be careful with the interpretation of estimates from Table 3.7 for three reasons. First, the Multifiber Arrangement (MFA) ended in 2004, and Cambodia was one of the countries that received increased global investment and increased apparel exports when the MFA ended (Beresford 2009 and Savchenko et al. 2012). Second, the dependent variable is for Cambodia’s total exports in terms of products and countries (i.e., exports to the world). Finally, these results are unlikely to be causal because there are no provisions in the TIF that reduce trade costs.

The third dimension of Cambodia’s trade that might be relevant is the relationship with China. In terms of trade agreements, the relationship between Cambodia and China through ASEAN is already captured in the previous estimation. In addition, exports to China [from Cambodia] remain a relatively small share of Cambodia’s total exports. Figure 3.1 shows the share of total exports from Cambodia, measured as imports from Cambodia by all other countries by year in the CEPII gravity database. It shows that, even when including Hong Kong SAR, China, with China, Cambodian exports to China (and Hong Kong SAR, China) are very small relative to those to the United States, Europe, and the rest of the world.

**Figure 3.1: Share of total exports from Cambodia**

<!-- Start of picture text --> 1995 2000 2005 2010 2015 2020<br>Year<br>USA Europe<br>China Japan<br>SEAsia Rest of World<br>.6<br>.4<br>.2<br>Share of Cambodia's Exports<br>
1

# I. Introduction

Recently, notable attention has been lent to the role played by minimum wage (MW) in the process of macroeconomic adjus'tuen*. The current professional wisdom highlights the important effect of MWa in raising total labor costs and in contributing to open unemployment. Moreover, in a dynamic context, it is believed that MWs introduce rigidities in the wages structure across industries, thereby hindering smoother intersectoral labor reallocation. The policy implication has been rather straightforward: maintain a low real MW.

Although a great deal is known about the macroeconomic effects of the MW, our understanding of how they work in terms of microeconomic foundations is rather limited. In particular, the effect of MWs on the unemployment and on the employment probabilities of specific labor force groups has not been clearly assessed in the case of developing countries. Our empirical knowledge on this issue has been strongly influenced by the statistical evidence obtained from industrial countries which, in turn, has notably relied on indirect estimating techniques.

In this paper, we undertake a statistical analysis of the effect of MWs on different population groups. The underlying question for this analysis relates to the probable bias exerted by certain protective government regulacuon in terms of the unemployment prospects of specific groups. We use several cross-sectional data and a standard human capital model corrected for selectivity bias to analyze the case of Chile, where high structural unemployment has been a remarkable feature of the 1970s and 19809. Our main conclusions show that the coverage of the MW is more s.gnificant for the young and the less educated, and there exists a negative relationship between human capital stock and actual coverage of MWs. We also show that previous
Das and Desai (2003) and Das (2006) had argued that in fact, cultural and structural factors are mutually reinforcing and the context that arises of the reinforcement hinders both women’s labor supply decisions as well as the demand for their labor.

The puzzle we focus on in this paper is why urban mothers in the 25-55 age group, who are more likely to be educated, have fewer children and reside in locations that are considered hubs for jobs, have had labor force participation rates consistently below 25 percent. It may entirely be possible that these women want to stay home with their children, but we believe that reality is more complex than that. There is little empirical evidence on the manner and extent to which urban mothers’ caregiving roles impede their ability to undertake market work. This paper therefore fills a critical gap in policy research on women’s employment in India. Situating the discussion in the normative construction of motherhood and gendered nature of caregiving in India, it uses pooled data from six rounds of the NSS to examine the effects of having a young child on mothers’ employment in urban India over the period 1983-2011. Second, it looks at household structure and analyzes the effects of other household members on women’s labor supply. The remainder of the paper is organized as follows. The next section is a review of studies on the construction of motherhood, employment and the care of young children. Section 3 presents data and descriptive statistics. Section 4 lays out the analytic strategy and methods, section 5 describes the empirical findings, while the sixth and final section presents a discussion of the findings and implications for policy and action.

# **2.  Motherhood, the Care of Young Children and Women’s Work**

Constructions of motherhood vary across cultures, but for the most part, mothers are the primary caregivers of young children. Globally, women with a child of pre-school age are less likely to be employed, provided other conditions remain the same.<sup>2