rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2.7: Imports’ composition is dominated by intermediate goods.** Cambodia’s Imports in Value (% of total), 2000 and 2020

<!-- Start of picture text --> 60<br>Import's share in 2000<br>50<br>Import's share in 2020<br>40<br>30<br>20<br>10<br>0<br>% of total imports<br>Textiles Agriculture Stone Minerals Metals Chemicals Vehicles Machinery Electronics Other Services Capital goods Consumer goods Raw materials Intermediate goods<br><!-- End of picture text -->

**Source:** Author’s elaboration based on data from the Atlas of economic complexity and WITS.

# **_Labor Market Trends_**

Cambodia's labor market has undergone significant structural changes over the past decade. Data from the household Cambodia Socio-Economic Survey (CSES) shows that the proportion of workers in agriculture declined from 58 percent in 2009 to 37 percent in 2019, accompanied by increases in services and industry employment. Construction, apparel manufacturing, financial, insurance, and real estate services were the fastest growing. For example, the employment share in apparel doubled from 5 percent in 2009 to 10 percent in 2019. Despite significant improvements in labor market outcomes, challenges in terms of gender-segmented labor markets and wage gaps still remain unresolved.

In conjunction with sustained growth in total trade since 2000, the broad range of labor market indicators reflects the general health of Cambodia’s labor markets. Between 2009 and 2019, Cambodia sustained high and stable labor force participation and employment, averaging about 84 percent (Figure 2.8). While rural labor participation rates remained largely unchanged, the urban rates increased approximately 8 percentage points from 73.4 percent in 2009 to 81.7 percent in 2019 (Figure 2.9).

6 | P a g e
4. Assessing (a few) variations in the assumptions of the CBN methodology Without a doubt, the application of the CBN methodology relies not only on the availability of very detailed information on households’ consumption and expenditure patterns, but also on a series of decisions that can potentially affect the ultimate result. In the section above, we showed that our results are robust to the expansion of the calorie-food mapping between POF and TBCA. We also find that applying a parametric or nonparametric approach to the estimation of the lines does not affect the results greatly.

Following, we continue exploring the potential effects of changing five different assumptions of our main specification: energy requirement, treatment of food away from home, reference group, incorporating implicit rent, and the treatment of lumpy items.

Energy requirement The energy requirement of an individual has been defined by WHO (1985) as that level of energy intake from food that will balance energy expenditure when the individual has a body size and composition consistent with long-term good health and that will allow for the maintenance of economically necessary and socially desirable physical activity. Our main specification uses a 2,100 calorie intake because we believe it to be a good benchmark. It is also placed roughly in the middle of several sources consulted (Table 1).

We explore how changing such a benchmark could influence our results. The US National Research Council (1989) estimated energy requirements by age group and sex. Based on this, and using the detailed data on Brazil’s demographic profile from the POF 2017/18 data, we calculate a population-weighted average required calorie intake. Moreover, this requirement could be estimated for the bottom 40 percent of the population – in other words, our reference group. As shown in Table 8, an estimate for the required calorie intake of a demographic population that resembles the bottom 40 percent in Brazil would be 2,268 kcal.<sup>16</sup> We round the caloric needs benchmark down to 2,250 kcal per day and re-run
0.0453|0.0416|0.213|0.0435|0.0537|0.330|0.0964|0.0663|0.312|0.0588|
|Overall|0.00414|0.283|0.0866|0.00302|0.266|0.0789|0.0427|0.375|0.133|0.00611|0.363|0.103|
|Observations|959|959|959|959|959|959|959|959|959|959|959|959|

Notes: (1) Panel Fixed Effects; All regressions include year fixed effects; (2) Anchored Relative Poverty line (60% of median at 2011); (3) Data Source: Farm Structure Survey, Eurostat; EU-SILC, Eurostat ; (4) Symbols: ** P<0.01, * P<0.05, +P<0.1 In a similar way as with poverty, our analysis also finds a significant impact of the CAP on the dynamics of inequality within regions in the EU. For this purpose, we first draw on the Gini index to measure inequality. Starting with per capita total payments, we find a strong and significant negative effect of such payments on the increase of inequality as measured by the Gini index. When analyzing each of the pillars separately, their individual effects on the decrease in inequality remain significant, particularly for pillar 2. Further disaggregating each of the pillars by their payment components gives no additional information on the particular performance of any of them in terms of inequality. All the results described remain qualitatively similar when we use the Theil index as our measure for inequality.

16
recent papers measuring the effect of _SP_ on labor market outcomes in Mexico. It then discusses relevant papers which evaluate the impact of similar programs in other countries. Overall, there is suggestive evidence that _SP_ may discourage formal employment, but data limitations, endogeneity, and omitted variables has left room for a more rigorous testing of the hypothesis.

Four established working papers are particularly relevant for our study. Two found no effect of _SP_ on labor market decisions, while two provide suggestive evidence of an effect – although using aggregate data or data only covering the Federal District.

Campos-Vazquez and Knox (2008) use aggregate data from 33 urban cities from the ―Encuesta Nacional de Empleo Urbano‖ (ENE) during the period 2001-2004. They do not find any effect of _SP_ on the rate of formal employment in the municipality. They also conduct a parallel analysis using individual-level data from 136 municipalities during 2002-2004 that also fails to find a significant effect. However, their data is only available for individuals from the poorest deciles, thus it captures a segment of society that already has high levels of informality and where it would be less likely that _SP_ would have much effect. Another potential problem is that their period of study may be too premature to find any effect at all.

Parker and Scott (2008) use Rand‘s Mexican Family Life Survey 2002 – 2005 panel. They do find a disincentive effect in rural municipalities, with the percentage change relatively large due to the small base, i.e. the absolute magnitude of the change is small. However, they do not find comparable effects in the urban areas. Using aggregate data from the 2000 and 2005 census, they do not find significant effects. This work has only a limited time dimension and is again looking at the early years of the program.

Barros (2008) measures the effect of _SP_ on health, consumption, and labor outcomes. He estimates the _SP_ effect by using a triple difference equation, taking differences over time, state intensity target (stated
15 While it is well documented that formal bank credit is seldom available to African farmers for input purchases, the working hypothesis is that farmers use tied credit with output and input traders and other sources of informal credit to finance the purchase of external inputs, while processors front inputs or cash for inputs in case of contract farming and cash cropping. The findings presented here contradict this, pointing to the important role of off‐farm income and crop sales instead. While this should not be taken as proof of credit constraint as such, it does highlight the importance of the nonfarm sector for agricultural modernization and the intimate links between agriculture and off‐farm employment, to which we return below. Broader (nonagricultural specific) rural development investments and policies will benefit agricultural development through different channels. In fact, the most common purpose of credit to a farming family in Africa is to finance the start‐up costs of non‐farm enterprises (or to finance consumption). This may be partially to help finance input purchases and increase agricultural productivity, an area for further investigation.

# _Market participation is widespread, but the extent of agricultural commercialization remains limited, without_

_clear benefits for nutritional outcomes._ Taking the share of the gross value of crop sales to the gross value of total agricultural production, i.e. the crop commercialization index, as their measure of market participation or agricultural commercialization, Carletto, Corral and Guelfi (2017) find that farmers sell on average around 20‐25 percent of their crop output (a bit less in Malawi, slightly more in Uganda and Tanzania). Conditional on sales the rates amount to 20, 40 and 33 percent in Malawi, Uganda, and Tanzania respectively, indicating that while most farmers sell some crops in these three countries, the marketed shares remain limited. Conditional on planting and selling, 11, 37 and 30 percent of the value of _food_ crops is commercialized in Malawi, Tanzania and Uganda respectively (Figure 6). Unsurprisingly, commercialization rates rise with harvest size
Table 10: Detailed Value Added Composition

||value<br>added<br>total<br>economy|gross exports<br>share of<br>total value|value added<br>in exports:<br>forward<br>linkages|value added<br>in exports:<br>backward<br>linkages|
|---|---|---|---|---|
||A|B|C|D|
|primary sectors|0.03|0.01|0.03|0.01|
|processed foods, beverages|0.03|0.04|0.04|0.04|
|textiles and clothing|0.02|0.06|0.04|0.06|
|chemicals, petrochemicals|0.03|0.13|0.08|0.09|
|motor vehicles|0.01|0.07|0.02|0.05|
|other machinery|0.07|0.23|0.15|0.23|
|other manufacturing|0.10|0.18|0.20|0.18|
|utilities, construction|0.12|0.01|0.05|0.01|
|commercial services|0.36|0.28|0.37|0.31|
|public services|0.23|0.01|0.02|0.01|
|total|1.00|1.00|1.00|1.00|

_source: Own estimates from CGE model and database._ we see the implications of productivity gains in services, combined with the tiersector linkages summarized by Table 4. Comparing Tables 4 and 10, we see the drop in value added for commercial services drives the shift shown in Table 9. The efficiency gains in
of a discriminatory education system. For the attentive observer, these issues are still felt today.

Table 2 shows that the country still performs poorly for indicators of education and health, and ranks highest among states in the region for child malnutrition, despite some improvements over time.<sup>8</sup> In addition, Guatemala spends less on education than any other country in the region. Based on household survey data comparing the education level of age cohorts, the Inter-American Development Bank (2001) finds that the educational gap between Guatemala and other Latin American countries is widening.

**Table 2. Guatemala, Central and Latin America: Comparison of Human Capital Indicators, 1998-2002**

|Indicator<br>G|uatemala|Nicaragua|Honduras|El<br>Salvador|Costa<br>Rica|Mexico|Latin<br>America|
|---|---|---|---|---|---|---|---|
|Public spending on education<br>(in percent of GDP) (average<br>1998-2000)<sup>c/ d/</sup>|1.7|5.0|4.0|2.3|5.7|4.4|N.D.|
|Average years of schooling<br>(2000)<sup>b/</sup>|4.8|6.3|5.3|5.1|6.7|7.9|7.3|
|Net primary school<br>enrollment (in percent)<br>(2000-2001)<sup>c/</sup>|84|81|88|81|91|103|97|
|Net secondary school<br>enrollment (in percent)<br>(2000-2001)<sup>c/</sup>|26|36|N
of these declines were found among emerging and developing countries: inequality fell in 10 African and 11 Latin American countries, for example. Of course, as the World Bank study acknowledges, in many developing countries there are serious concerns about data quality and comparability, which make reliable analysis difficult. Yet, this is not true in all cases and, in those countries where the data are reliable and where the inequality trends are markedly different from those in the United States and Western Europe, it would seem possible to learn much that is of general interest.<sup>2</sup> It is therefore surprising that so little attention has been given in the mainstream

> 1 See, for example, Bound and Johnson (1992), Katz and Autor (1999), Acemoglu (1998 and 2002), and Card and DiNardo (2002) among many others. More recently, changes in the distribution of occupations and, in particular, in the role that different tasks within occupations have on wage determination, have become central to the discussion of technical change (e.g. Autor, Levy and Murnane, 2003).

> 2 This argument is analogous to the suggestion by Hamermesh (2004) that much of general interest could be learned from studying the determinants of labor demand in developing countries: “It is true that in many cases the data on labor markets in developing countries are not as complete as in developed economies, but in some cases they are, and in those instances the availability of good data that cover periods of widespread policy

2
For the impact evaluation of the concurrent trial, one primary or secondary health facility was randomly chosen per ward in each of the districts, for a sample of 786 facilities out of the 1,389 facilities participating in the trial. At each facility, two health workers who routinely provide antenatal or under-five curative care from the roster of health workers present on the day of the survey were randomly sampled for an in-depth interview. We conducted our experiment in all 691 primary health facilities in the survey sample of the impact evaluation. All health workers in a selected facility were eligible to participate in the experiment. The experiment was conducted simultaneously with participating health workers in the same facilities being interviewed in different rooms to prevent information sharing.

The survey instrument captured a range of health worker characteristics, including their education, when they have been last trained in labor and delivery, and their professional grade. In addition, the survey included two standard assessments of worker knowledge and skill (see for instance, Das and Gertler, 2007) in the preceding non-experimental section. First, it included a protocol-based vignette based on the standard WHO antenatal care protocol (Villar et al., 2001) in which participants were read a narrative about a pregnant woman seeking antenatal care and were asked to list everything that they would do during that visit.

Second, the survey provides a measure of real effort—i.e. one with costs to the health worker— through direct observations of actual patient-provider interactions in the context of antenatal care. As described in Kandpal et al. (2019), direct observations were performed at a randomly-chosen third of the sampled primary health centers. In each health center, one randomly selected health worker was observed while she was providing antenatal care to two patients. We thus have direct observation data for 339 of the 1,363 health workers in our experiment. The observation was conducted using a structured, quantitative checklist and the data were collected by enumerators trained in the direct observation of antenatal care provision. Enumerators recorded whether
_(Oxford tracker)_|-0.0526<br>(00316)||||||||||||
|<br>Stringency Index|.|-0.0505*|-0.0502*|-0.0539*|-0.0531*|-0.0556*|-0.0509*|-0.0571**|-0.0788**|-0.0570**|-0.0686**|-0.0470*|
|_(Oxford tracker)_||(0.0291)|(0.0271)|(0.0280)|(0.0276)|(0.0300)|(0.0265)|(0.0266)|(0.0302)|(0.0265)|(0.0296)|(0.0274)|
|_Regional Dummies_|||||||||||||
|Dummy: Sub-Saharan|2.287*|1.967|2.463*|2.208*|0.412|1.435|1.857|1.274|3.520**|3.104**|2.477*|2.029|
|<br> Africa (SSA)|(1.261)|(1.377)|(1.291)|(1.256)|(1.154)|(1.403)|(1.144)|(1.154)|(1.363)|(1.285)|(1.357)|(1.497)|
|Observations|88|79|88|88|93|81|93|93|78|89|79|88|
|R-squared|0.39|0.483|0.440|0
2016).

# III.2.Data Capacity and Transparency

The main variable of interest in our paper, data capacity and transparency, is proxied by the World Bank’s Statistical Capacity Index (SCI). The overall SCI score is based on a diagnostic framework to assess the capacity of national statistical systems over time. The framework has three dimensions: source data; methodology; and the periodicity and timeliness of socioeconomic indicators. A composite score for each dimension and an overall score combining all three dimensions are derived for each country on a scale of 0–100. A score of 100 indicates that the country meets all criteria. Each dimension is evaluated on criteria based on metadata information obtained from the World Bank, International Monetary Fund, United Nations, the UN Educational, Scientific and Cultural Organization (UNESCO), and the World Health Organization (WHO).

The overall SCI score is the average of the three sub-indicators calculated for each dimension (see Table 1). The source data dimension reflects whether a country conducts data collection activity in line with internationally recommended frequency (periodicity), and whether data from administrative systems are available and reliable for statistical estimation purposes. This dimension covers the micro-data aspect of data transparency because microdata is at the foundation of a country’s data system. Specifically, the criteria used are the frequency of population and agricultural censuses and of poverty- and health-related surveys, and completeness of vital registration system coverage. A country can achieve a perfect score if it has conducted at least one population census in the past 10 years, one or more agricultural censuses in the past 10 years, three or more health surveys in the past 10 years, and has a complete vital registration system.

The statistical methodology dimension measures a country’s ability to adhere to internationally recommended standards and methods. This aspect assesses guidelines and procedures used to compile macroeconomic statistics and for social data reporting and estimation practices. This dimension measures 7
is the elasticity of substitution between college and high school graduates. In a competitive labor market, the college premium is equal to where _wt_<sup>_g_and</sup><sup>_w_</sup> _t_<sup>_h_arethewagesofcollegeandhighschoolgraduates,respectively.Takinglogs</sup>

> 28Ferreyra et al (2021) shows that students in short-cycle programs graduate at higher rates than those in bachelor’s programs, and that their graduates obtain better labor market outcomes than dropouts from bachelor’s programs.

44
# 1. INTRODUCTION AND SUMMARY<sup>2</sup>

One enduring feature of the development process, highlighted by Lipton (1977), is that rural populations are lagging behind their urban compatriots – this observation also applies to Millennium Development Goal (MDG) indicators (Figure 1.1). This paper is focused on policies aimed at closing this gap for one of the MDGs, the under-five mortality rate (U5MR or MDG 4), trying to shed light on what may be required to raise the rural population up to the urban level for this indicator or, more modestly, in terms of the government health services that reach them. The size of the effort that is required to achieve such objectives depends on a number of structural features of the modeled economy, including the rate of urbanization, rural-urban cost differentials for health services, and the magnitudes of initial rural-urban gaps in terms of the indicator itself, the U5MR, and its determinants, including access to health services and other aspects of rural living conditions.

Figure 1.1. Rural and urban MDG data for low-income countries in 2009 Analytically, this paper relies on MAMS (Maquette for MDG Simulations), a computable general equilibrium (CGE) model, applied to the database of an archetypical low-income country (LIC). The rationale for choosing a general equilibrium approach is that, in the presence of large ruralurban gaps and large rural population shares in most LICs, it would be misleading to limit an analysis of policies aimed at achieving such objectives to a partial-equilibrium framework: given macroeconomic and market constraints, required changes in government activities (spending level, spending efficiency, or revenue level, from domestic or foreign sources) would have effects that make themselves felt throughout the economy, often pointing to trade-offs and

> 2 The paper was prepared as background to Global Monitoring Report 2013. The author is indebted to the Knowledge for Change Program (KCP) Trust Fund for funding of the MAMS-based research program and is grateful for research support from Eugenia Suarez Moran
offers a different set of measures of poverty mobility: conditional probabilities, _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), represent the likelihood of an individual’s status in the second period, _given_ their status in the first period. The first line of Table 6 refers exclusively to people who were classified poor in 2017: out of this group, how many remained poor in 2022, and how many managed to escape? A similar interpretation can be applied to the second row, which focuses on people who were not poor in 2017: out of this (much larger) group, how many fell into poverty in 2022, and how many remained above the poverty line? Clearly, this implies that the row-wise sum of probabilities in Table 6 is 100.

The takeaways from Table 6 go along with those discussed for Table 5 – after all, the trajectories underlying the measures presented are the same – but they provide a different point of view, one that allows for a sharper focus on the poor. In fact, while the share of chronically poor individuals is relatively low as a proportion of the country’s population,

> 7 National population from the World Bank World Development Indicators.

13
fi cant fi xed rent effect (p-value = 0.303).

The curious fi nding that share-tenants do not invest any less than fi xed rent tenants, conditional on tenancy duration, might be explained by a monitoring argument. Given moral hazard in current production effort, it may pay for a landlord to be involved in and to supervise his share-tenant’s production activities. Indeed, share-tenants in Pakistan are typically heavily supervised (see Nabi, 1986; Jacoby and Mansuri, 2004), either directly by their landlord or by hired labor managers (kamdars). If there are economies of scope in supervision, then the cost of monitoring a tenant’s investment effort (at least to some degree) is lower in sharecropping than in fi xed rental arrangements. Provided that this monitoring effect outweighs the moral hazard effect, share tenants may invest more than fi xed rent tenants, although an econometric investigation of this hypothesis is beyond the purview of the present paper. Our main result here is that tenants of either type, unequivocally, invest less than owners.

> 35 The fi rst two of these village level variables are computed directly from the full survey sample, whereas the land gini is based on a census of landownership for the entire population of each village. Since these 6 new instruments hardly add any explanatory power to the fi rst-stage regression for the overall leasing decision, we did not use them in earlier IV speci fi cations.

21