rafmacalaba/data-use-mentions-extended huggingface dataset:
rates, underemployment, and food insecurity (e.g., WFP 2012; World Bank 2017a; etc.). Furthermore, there was a broad agreement between household heads and spouses in the average levels of satisfaction, which is also slightly surprising given significant evidence of intra-household inequities (e.g., World Bank 2012).

However, there is an important data issue to highlight that might affect estimating the impact of capturing Sana‘a. Ramadan occurred primarily in July in 2014, and this might have biased peoples’ subjective assessment of their welfare given different consumption patterns and household choices during that month (e.g., Jolliffe and Serajuddin 2012; World Bank 2017a). Thus, baseline specifications exclude households surveyed during Ramadan.<sup>17</sup><sup>_,_18</sup>

# **5. The Impacts of the Capture on Subjective Assessments of Welfare**

Using the 2014 HBS, we estimate how much more the share of household heads reporting to be either “very satisfied” or “fairly satisfied” increased in Sana’a relative to the rest of the Republic of Yemen following the capture of the city. Specifically, for each welfare dimension, we estimate the following specification: where _Satisfaction_<sup>_j_</sup> _i_<sup>isanindicatorequalingoneiftheheadofhousehold</sup><sup>_i_iseither</sup> very or fairly satisfied with dimension _j_ ; _Sana_<sup>_′_</sup> _ai_ is an indicator if the household lives in the capital city; _Posti_ is an indicator if the household completed their final interview before Sana’a was captured by Houthi forces; and _Xi_ is a set of control variables.<sup>19</sup> The estimate of _α_ is by how much the share of households satisfied with welfare dimension

> 17However, all results are qualitatively identical when the excluded households are included, and these estimates are included in the Appendix.

> 18This reduces the total sample size
# **1. Introduction**

Low student learning is an important feature in Pakistan, as it is in much of the developing world (Andrabi et al. 2007; Glewwe and Kremer 2006; Hanushek and Wößmann 2007). The shortfalls relative to national curricular standards are exacerbated by the fact that a large share of children, particularly rural, female, and poor children, obtains little or no formal schooling in the country, likely seriously impairing their long-term socioeconomic prospects. In addition, in general, the factors and processes that produce real and sustained gains in student learning are not as well understood as the factors and processes that produce real and sustained gains in school participation and attainment. Evidence also shows that the factors and processes behind learning can differ from the factors and processes behind participation (Andrabi et al. 2007). This background makes the search for effective policy solutions to improve learning outcomes pressing yet tenuous.

In this study, we use five semiannual rounds of standardized test data to characterize and explain the evolution of student learning over a period which spans three academic years (2007/08-2009/10). The tests were conducted in low-cost private schools supported by public cash subsidies under an innovative test-based accountability program in the province of Punjab, Pakistan administered by the Punjab Education Foundation (PEF), called the Foundation Assisted Schools (FAS) program. The data are repeated cross-sections at the student level and longitudinal at the school level. Such data are relatively rare for a developing country. Additionally, the data are unusual for a low-income setting given that they are generated from what appears to be a well-designed and robust quality assurance testing system integrated into the administration of the program, providing a high degree of confidence that the test scores can be interpreted as sound estimates of underlying learning.

Initiated in 2005, the FAS program provides conditional cash subsidies to low-cost private schools with the objective of offering private schooling opportunities for children from low-income households and raising the level of learning in low-cost private schools. The cash subsidies
cantly larger than 1 would suggest that a recipient government matches on-budget health aid by increasing its own health expenditures.

To determine the degree of fungibility of off-budget health aid, however, we must compare _β_<sup>�</sup> _OFF_ to a different benchmark. Because off-budget health aid is not considered part of a government’s health expenditure _HSP_ even if there is no fungibility, a lack of fungibility for off-budget health aid occurs when _β_<sup>�</sup> _OFF_ is greater than or equal to 0, not 1. Off-budget health aid is fungible if _β_<sup>�</sup> _OFF_ is negative. For instance, if a donor finances the building of new hospitals with off-budget health aid, then fungibility would occur if the recipient government reacted by building fewer hospitals and reallocating some of its health spending to other sectors. In that case, the off-budget health aid of the donor is at least partly fungible because the total amount of resources devoted to the health sector (the sum of government health spending and off-budget health aid) increases by less than the amount of off-budget health aid.<sup>2</sup> Full fungibility occurs if _β_<sup>�</sup> _OFF_ is not greater than the marginal effect of unconditional resources _R_ minus 1, whereas a significantly positive coefficient for _HAIDOFF_ constitutes evidence of matching behavior by recipient governments.

We are now in a position to discuss how previous studies may have produced biased fungibility estimates. Some studies have relied on aid data reported by donors. These data are either collected directly from donors or obtained from databases managed by the OECD’s Development Assistance Committee (DAC) (e.g., McGuire, 1982; Khilji and Zampelli, 1994; Pettersson, 2007a,b). In this case, an equation of the following form is estimated: where _HAID_ = _HAIDON_ + _HAIDOFF_ is the total health aid, the sum of on- and off-budget health aid. The estimated marginal effect of
# **_5.2 Effects of policies on misallocation_**

To assess the effects of these reforms, we build a panel of industries across states and compute changes in output misallocation between 1989 and 2000. This is our dependent variables in the regressions reported in table 12. Note that relative to our analysis of services in section 4 or to the results presented in Duranton _et al._ (2015), we work with different units, industries within states instead of an aggregate of industries within districts. Below, we assess to what extent this different level of analysis affects our results.

Our main explanatory variables are the reforms described above, which we aggregate over 1985-1997. Hence, our estimations regress changes in misallocation between 1989 and 2000 on institutional and policy changes between 1985 and 1997. We also consider longer lags for changes in misallocation below. Because changes in misallocation are often noisy and because the initial level of misallocation may be correlated with what triggered some of the reforms, we also control for the initial level of misallocation in our regressions.

From the regressions reported in table 12, we can draw a number of conclusions regarding the effects of institutional and policy changes. The first one regards the effects of labor reforms. Stricter pro-worker regulations led to a worse misallocation. This result is robust across most It is when we break down our and specifications. only sample distinguish between the organized and the unorganized sector that the effect of labor reforms become insignificant. This is perhaps unsurprising since labor reforms are thought to have pushed many establishments into the unorganized (informal) sector (Besley and Burgess, 2004).<sup>13</sup> More generally, this negative effect of pro-worker labor reforms is fully consistent with the conclusion of Besley and Burgess (2004) who find that these reforms were generally harmful to industrial development in India.

The second strong result that emerges from table 12 regards land reforms. These reforms led to less misallocation in manufacturing. Like with labor reform, it is only when we
<br>household reported income loss relative to prepandemic in<br>a previous HFPS wave (panel households)|23|56,360|
|**Food security**|Household adults went a full day without eating in the last<br>30 days|61|309,457|
||Household received any form of public assistance since the<br>start of the pandemic|70|359,736|
|**Assistance**<br>**di**|Household<br>reduced<br>consumption<br>of<br>goods<br>during<br>the<br>pandemic|49|229,090|
|**an copng**<br>**strategies**|Household used money saved for emergencies to cover basic<br>living expenses|52|285,059|
||Household sold assets such as property to pay for basic living<br>expenses|51|283,790|

_Notes_ : The number of observations includes all survey rounds; questionnaires varied across survey rounds and countries.

and individuals across panel survey rounds where possible. This both minimizes the recall period and reduces potential variation in recall bias across survey rounds. We observe that prepandemic employment estimates are stable across surveys, suggesting the constant recall bias assumption is valid in our data (Figure A3.2).

A further cross-check on employment statistics from HFPS can be provided by comparisons with labor force survey (LFS) data, primarily in UMICs and high-income countries (HICs) that continued to collect LFS after initial interruptions due to the pandemic. It 11
investments in health in the context of intervention projects (es EPI evaluation, case-control studies of diarrhoeal disease control measures etc.).

# 2. INVESTIGATION OF PATTERNS AND DETERMINANTS OF HEALTH

# 2.1 Existing health ipXz ation svstcms in developing countries

The first objective of the proposed WHS is to 'improve knowledge of patterns and determinants of health, disease and mortality in developing countries.' Developing countries are well aware of their lack of appropriate quantitative measures for planning, monitoring and evaluation in the health area. Many have been making strenuous activities to improve knowledge of mortality levels and differentials with additional questions in censuses, post enumeration surveys or special household studies. In another direction, most are striving to improve the accuracy and coverage of their vital registration siz*+ems. These two developments have in most cases taken place independently from the health sector with consequent loss of efficiency and of the gains which would certainly flow from comparison and combination of information from different sources.

A second area of activity is the improvement of routine health services reporting. This integrates the provision of information with the provision of services. As a result patterns and trends in health can readily be linked to input and output evaluations of the operational efficiency of programmes. With appropriate information systems, findings can readily be fed back to staff involved in providing health services and in collecting data. However such reporting systems have inherent disadvantages. Most notably they only provide information on that part of the population that is covered by and uses the health services. The poorer, rural and less educated sections of the population tend to be excluded from such statistics. By definition, they cannot measure unmet needs for health care. Secondly service-based reporting systems provide information on case 7
2012; Jones et al., 2014). The approach adopted by Uwezo has been inspired by exercises carried out in India by the Assessment Survey Evaluation Research Centre (ASER), which has surveyed the literacy and numeracy abilities of over 500,000 children each year since 2005. As with ASER, the target population of the Uwezo surveys has been children (residing in households) aged between the official starting-school age and 16. The surveys have been designed to be representative at both national and district levels, based on the administrative 13 classifications in the most recently available population census. Excluding the initial surveys, five rounds of the Uwezo surveys have been completed (2011-2015) and are used here.

In each assessment, the surveys collected information at the household level covering household characteristics and the demographic and educational details of all resident children (e.g., age, gender, whether or not attending school, etc.). Within each household, the children of school age were individually administered a set of basic oral literacy and numeracy tests. These tests have been based on a common template, but have been tailored to each country and varied by survey round. Specifically, in each round and country, local experts have taken the template and developed item content to reflects competencies stipulated in the national curriculum at the grade 2 level. That is, the tests are anchored to skills that should be achieved by the majority of pupils after two years of completed schooling.

The literacy and numeracy tests (the Uwezo tests) are described in detail in Jones et al. (2014). The literacy tests refer to national languages of instruction in which pupils are tested at the end of primary school – i.e., English and Kiswahili in Tanzania and Kenya; and just English in Uganda. Importantly, the Uwezo tests are not adapted to the children’s ages or their completed level of schooling. Given that they focus on basic competencies, it is thus unsurprising there are strong age- related differences,
61 the satellite analysis, tea cultivation areas are held fixed to the 2021 tea map, which is applied to every year. The underlying assumption is that tea planted areas do not change on an annual basis because tea is a multiyear permanent crop. We also generate a derived confidence index reflecting the precision of the satellite-derived tea maps, which we use as statistical weights in our tea yield regressions in Appendix C.D. To obtain the confidence index, we first derive a tea probability map using an ensemble random forest image classification algorithm applied to tea data. In this map, any pixel with less than 50% probability is identified as a non-tea planted pixel. We remove all non-tea planted pixels, leaving only pixels with probabilities between 51% and 100%. We then average these probabilities across the pixels within each DS and round the resulting value to obtain the confidence index (ranging from 50 to 100) for each DS.

# **A.D Agricultural Prices**

To estimate our model, we use data on producer prices for each crop (except tea) and district from the 2016-2019 edition of DCS’ Bulletin of Selected Retail and Producer Prices.<sup>101</sup> We extract average producer prices (in LKR/kg) in 2019 for rice, potatoes, cinnamon, cloves, onions, groundnuts, and maize. For onion and rice, whose prices are available for multiple varieties, we use the prices of red onions and red rice (raw). Whenever the producer price of a crop is missing for a given district, we impute it using the crop’s average price across other Sri Lankan districts. Producer prices are missing for 45% of the district-crop pairs with non-zero agricultural production. Nevertheless, the imputation procedure is unlikely to create biases since producer prices are similar across space: the coefficient of variation of (non-missing) producer prices across districts averages 0.032, ranging from 0.009 to 0.058 across crops. For tea, we use
predictions. They hypothesize that if education quality is a normal good, the existence of private education leads parents past a threshold income to exit the public sector. This is the income at which the cost of private school tuition is just offset by the utility gain from consuming higherquality education. In societies with higher income and more unequal income, more people use private schools, and thus vote for low or no spending on public education. Under majority voting, this means less public education. Stiglitz (1974) showed that preferences are not singled-peaked when private education is available. Several approaches address this and obtain existence of a majority voting equilibrium: imposing a single crossing property in a median voter model (Epple and Romano, 1996; Gutirrez and Tanaka, 2009), identifying the decisive voter (Barzel, 1973; Glomm and Ravikumar, 1998), and using a probabilistic voting model (de la Croix and Doepke 2009). In the next section, I present a model similar to de la Croix and Doepke (2009). However, the reader should be aware that a variety of modeling assumptions deliver similar results.

The second literature describes a “political power” channel. It finds that the rich exercise control over public policy that is roughly proportionate to their share of income (or a function of it), as opposed to their share of votes. Chambers (1983) argues that the inability to influence policy is a defining feature of relative poverty. A number of papers show that inequality reduces the political influence of the poor. It is less clear what is the effect of higher median income. However, the _key_ implication of papers in this literature is that the distribution of income _affects_ policy, by affecting the distribution of political power and influence.

Several prominent empirical studies describe the political power channel. Foster and Rosenzweig (2004) use panel data on villages in rural India to show how the share of the population made up of landless poor affects public investment when the village leader is or is not elected
higher followed by the “Professionals” and “Skilled agricultural, forestry and fishery workers”. On the contrary, “Managers”, “Technicians”, “Craft and related trades workers” and “Plant and machine operators and assemblers” are the types of occupations with a lower incidence of NSE in the region.

Figure 16: Prevalence of NSE among salaried employees. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 30%<br>starting point<br>25% ending point<br>20%<br>15%<br>10%<br>5%<br>0%<br>Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys

Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) <!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Managers Professionals Technicians Clerical Services and Skilled Craft and Plant and Elementary<br>Support Sales Agricultural Related Machine Occupations<br>Workers Workers Workers Trades Operators<br>Workers<br>Albania Armenia Georgia Kyrgyzstan Moldova Russia Turkey<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys.

As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “Professionals” and “Elementary workers” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard
have been far more important in Africa over the past half-century (see Croser and Anderson 2011).

**_Commodity export price index._** We construct a country-specific international commodity export price index for agricultural and natural resource commodities as:

_ComPIi_ , _t_  _ComPricec_ , _t_  _i_ , _c_  _c_  _C_ where _ComPricec,t_ is the international price of commodity _c_ in year _t_ , and _θi,c_ is the average (timeinvariant) value of exports of commodity _c_ in the GDP of country _i_ .<sup>9</sup> We obtain data on annual international commodity prices from UNCTAD Commodity Statistics and our data on the value of commodity exports are from the NBER-United Nations Trade Database. The commodities included in the agricultural commodity export price index are beef, coffee, cocoa, cotton, maize, rice, rubber, sugar, tea, tobacco, wheat, and wood. The commodities included in the natural resource export price index are aluminum, copper, gold, iron, and oil. In case there were multiple prices listed for the same commodity a simple average of all the relevant prices is used.

**_Rainfall._** The annual rainfall data are from Matsuura and Willmott (2007). The rainfall data come at a high resolution (0.5°x0.5° latitude-longitude grid) and each rainfall observation in a given grid is constructed by interpolation of rainfall observed by all stations operating in that grid. We aggregate rainfall data to the country level by assigning grids to the geographic areas of countries.

**_GDP per capita and other data._** Data on real per capita GDP, trade openness (exports plus imports over PPP GDP), private consumption per capita, investment, and the government expenditure share are from the Penn World Table, version 6.3 (Heston et al., 2009). Data on political institutions are from the Polity IV database (Marshall and Jaggers, 2009). The data on civil war incidence are
percent in Brazzaville and 31.5 percent in Pointe-Noire, compared to an unemployment rate of only 5.8 percent in rural areas and 9.0 percent in semi-urban areas.

Unemployment affects especially the youth. Countrywide, according to the 2005 household survey, the unemployment rate was highest for the 15-19 age group (above 40 percent) and declined gradually thereafter with unemployment rates of 36.7 percent for the 20 to 24 year old and 27.7 percent for the 25 to 29 old. In urban areas, according to an employment and informal sector survey carried out in 2009, 25 percent of the population under 30 years is unemployed if the ILO definition is employed whereas this rate increases to more than 40 percent if a broader definition is used (EESIC, Republic of Congo 2010a and 2010b).

**Figure 3:** Unemployment rates (in percent)

<!-- Start of picture text --> 30.9<br>19.4<br>16.1<br>13.1<br>12.2<br>1974 1984 1994 2005 2009<br><!-- End of picture text --> _Source:_ RGPH 1974, 1984, 1994, ECOM 2005, EESIC 2009 (urban areas)

# 2.2 <u>Infrastructure – bottleneck and potential growth source</u>

Access to and cost of infrastructure represent major constraints to private sector investment. In fact, according to the Enterprise Indicator survey carried out in 2009, more than 70 percent of private firms consider the lack of electricity as the main obstacle for private sector development in Congo (World Bank 2009, Figure 4). Power cuts have reduced firm turnover by 19 percent and 82 percent of firms have to rely on a generator. The quality of the fixed line telecommunication network is poor, and the international/regional connectivity is underdeveloped. The overall dilapidated infrastructure leads to exceptionally high input costs in all areas.

Poor transport infrastructure and transport services