rafmacalaba/datause-encoder-data huggingface dataset:
form mis-specication is not responsible for the observed relations.<sup>7</sup> Taking advantage of data on cognitive ability in IFLS 2014 in Indonesia, we explore whether the estimated eect of father's education is largely due to omitted ability heterogeneity of children. We nd that the inclusion of quadratic controls for ability reduces the magnitude of the impact of father's education on conditional variance, but the estimates still remain substantial and statistically signicant at the 1 percent level.

For relative mobility, the estimates of risk adjusted IGRC (RIGRC) suggest that the workhorse measure of relative mobility in the literature, IGRC, substantially underestimates the impact of family background. The estimates for the full sample (1950-1989 cohorts) suggest that the extent of underestimation on average is 26 percent in China, 41 percent in India, and 10.4 percent in Indonesia.<sup>8</sup> Accounting for the inuence of family background on conditional variance of schooling makes a dramatic dierence in the estimated relative and absolute mobility for the children born to the most disadvantaged households (fathers with no schooling).<sup>9</sup> RIGRC estimates from the full sample (1950-1989 birth cohorts) for this subgroup shows that the standard IGRC overestimates relative mobility by 37 percent in China, and by 63 percent and 28 percent in India and Indonesia respectively. In contrast, the gap between the RIGRC and IGRC estimates for the subgroup with college educated fathers is small. Absolute mobility is also substantially overestimated for the most disadvantaged subgroup without risk adjustments: conditional mean of years of schooling is overestimated by 48 percent in China, 127 percent in India, and 25 percent in Indonesia. Again, for absolute mobility of the children of college educated father, the risk adjustments does not make any substantial dierence. The upshot is that while the standard estimates of relative and absolute mobility seem to capture reasonably well

> 7Based on recent evidence, we allow for a quadratic mobility CEF in place of a linear functional form (see Becker et al. (2015, 2018), Emran et al. (2021), Ahsan et al. (2021)). We nd that allowing for a quadratic CEF does not change the relation between the conditional variance in children's schooling and father's education in any signicant manner.

> 8The smaller magnitude of underestimation in Indonesia despite a large inuence of father's education on the conditional variance noted earlier reects the fact that the ratio of the conditional variance to the conditional mean is much smaller. This ratio is important in determining the risk premium. Please see section (5) below.

> 9Note that IGRC, the measure of relative mobility in the workhorse linear model, does not vary with father's education level. But the risk adjusted measure RIGRC varies across low and high educated households because of dierences in the conditional variance and the conditional mean.

4
_Role of NGOs in Health Delivery_ _.3._ _Afdcr Health Policy Study_ of the prepondemnce of any given categories of NGOs. However, the dichotomies<sup>are</sup> relevant for analyzing a crucial constraint of NGO performance: from where do they obtain their funding, and as a result, to whom are they accountable? In contrast<sup>to</sup> government facilities which receive funding from only one source, the pattern of NGO funding tends to be much more diversified. As just one example of the consequences<sup>of</sup> types of organizational structure, one can contrast membership organizations (such<sup>as</sup> cooperatives or, at a national scale, the Christian health assoclations which act as umbrella organizations for NGOs) with organizations that depend on external voluntary assitance. The difference in accountability of the two types Is critical: whereas membership orgiwizations are accountable to local constituents on whom they depend for funding, inte ;,utional NGOs are beholden to the benevolence of a foreign (e.g., American, British, European, etc.) public.

# Changing Environment of NGTOs

7. Over the last decade the flow of resources destined to NGOs in developing countries has increased steadily. According to the OECD, private sources i DAC member countries are contributing more than US$3.5 billion annually to NGOs (OECD 1987 cited in Bratton 1989). As bilateral and multilateral levels of aid have decined, NGOs are donating a larger proportion of assistance to developing countries. A total of $1 billion is estimated to have reached Africa through NGOs in 1986 (Bratton 1989). Table I sl& changes in NGO aid in relation to official aid disbursements in 18 countries from 19L 1986, revealing a marked increase in NGO aid in all but five cases (ODI 1988).

8. Complete d-ta on flows of aid to the health sectorfrom through non-governmental agencies operating in Africa are not available, and the limited data which are available are subject to misinterpretation due to the lack of or discrepancies In the accoundng capacity of NGOs and the risk of double-counting of bilateral aid figures.<sup>2</sup> However, evidence suggests that the trend towards channeling funds through NGOs has also reached the health sector and there are currently substantial flows to NGOs engaged in health-related activities (Tchicaya 1988).

9. Unlike most "fashionse in development thinking, the recent surge in the popularity of NGOs is shared by all shades of the political spectrum. On the one hand, 2. Double-counting may occur, for example, when British Overseas Development Administration donates a proportion of its yearly budget to OXFAM or Save the Children. Similar problems arise in the U.S., where the government is often the main funder of NGOs. Certain NGOs, such as OXFAM in the U.K, have stpulated a ceiling on the proportion of funds they receive from government to maximize their autonomy from government.
Table 9: Bolsa Familia, Unemployment Insurance and Training Responses to GDP Decline in Firms’ Destinations

||Average<br>real wages<br>(1)|Average<br>number of<br>months in UI<br>(2)|Average<br>earnings<br>from UI<br>(3)|Average<br>number of<br>months in PBF<br>(4)|Average<br>earnings<br>from PBF<br>(5)|Indicator for<br>receiving<br>training<br>(6)|Indicator for<br>receiving<br>technical training<br>(7)|
|---|---|---|---|---|---|---|---|
|GFC frm shock*2009|-28.6391***|0.0104***|12.6110***|0.0073*|0.7037***|-0.0001|-0.0001|
||(6.3102)|(0.0031)|(3.1702)|(0.0045)|(0.2723)|(0.0004)|(0.0003)|
|GFC frm shock*2010|-28.4306***|0.0005|-0.6657|0.0128***|0.9880***|0.0004|0.0005|
||(6.3088)|(0.0020)|(2.0105)|(0.0046)|(0.2958)|(0.0004)|(0.0003)|
|GFC frm shock*2011|-28.3811***|-0.0008|-1.9912|0.0148***|1.2547***|0.0002|0.0003|
||(6.2924)|(0.0019)|(2.0108)|(0.0046)|(0.3350)|(0.0003)|(0.0003)|
|GFC frm shock*2012|-28.2186***|0.0008|0.2453|0.0146***|1.3997***|0.0001|0.0002|
||(6.2794)|(0.0018)|(2.0030)|(0.0046)|(0.3693)|(0.0004)|(0.0003)|
|GFC frm shock*2013|-27.9682***<br>(6.2490)|-0.0004<br>(0.0018)|-0.6452<br>(2.0402)|||||
|GFC frm shock*2014|-27.6736***|0.0002|-1.0784|||||
||(6.1863)|(0.0016)|(1.9543)|||||
|GFC frm shock*2015|-27.0546***|-0.0002|-1.1020|||||
||(6.1000)|(0.0016)|(1.9736)|||||
|GFC frm shock*2016|-26.9920***|0.0014|1.2943|||||
||(6.0603)|(0.0017)|(2.1482)|||||
|GFC frm shock*2017|-26.2731***|0.0023*|2.0523|||||
||(5.9758)|(0.0013)|(1.7051)|||||
|Observations|3,046,074|3,046,074|3,046,074|1,365,282|1,365,282|3,046,074|3,046,074|
|R-squared|0.4228|0.0266|0.0186|0.1337|0.0637|0.0367|0.0262|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. FE stands for fixed effects.

39
women of different wealth levels is driven by financial constraints or willingness to pay, rather than by knowledge of which facilities are better.

Table 5 investigates the extent to which wealth gradients exist within facilities. Within‐facility gradients could emerge due to discrimination if providers exert less effort when treating women with lower educational or socioeconomic status than theirs. Alternatively, within‐facility gradients could be a result of the separate fees some facilities charge for consultations, consumables, laboratory tests, and drugs. Poorer women might choose a more limited package of care, or providers might not offer services for which they believe poorer women would not be able to pay.

Table 5, columns 1 and 2, restrict the sample to women who reported attending the health center serving their catchment area and include catchment area dummies in the regressions. As the content of care reported in the household data is from the entire sequence of consultations during the women’s pregnancies, and wealthier women attend more consultations, we control for the number of consultations women reported attending. We observe a statistically significant within‐facility relationship between wealth and content of antenatal care. A standard deviation increase in wealth is associated with a 1.6 percentage point increase in the proportion of actions taken. When controlling for women’s characteristics, the coefficient only slightly changes to 1.4 percentage points (p‐value < 0.01).

Table C.5, in appendix C, presents the results of similar regressions with individual components of care as the dependent variables. We find a statistically significant association at least at the 90 percent level for six of the 13 elements of care reported by the women in the household survey. These elements include services that could require additional fees, such as HIV testing, iron supplementation, and antimalarial drugs. We also find a statistically significant relationship between wealth and receiving advice on nutrition during the pregnancy, having the due date estimated, and measurement of uterine height.

Table 5, columns 3 and 4, explores within‐facility wealth gradients using the direct observation data. A one standard deviation increase in wealth is associated with a 0.6 percentage point increase in completeness of care (p‐value < 0.05). The coefficient does not change when we add controls for women’s characteristics. When we focus on the individual elements of the content of care in the direct clinical observations, as seen in table C.6, in appendix C, we find a statistically significant relationship for a much lower share of the items and at a lower level than when we consider the items reported in the household survey. These items include hemoglobin testing, prescription or provision of iron, as well as 13
MINIMUM WAGES IN CAMBODIA 22 <!-- Start of picture text --> All<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of hourly wage distribution<br>Point estimates 95% CI<br>Female<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of hourly wage distribution<br>Point estimates 95% CI<br>.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br>Quantile treatment effects<br>-.06<br>-.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>-.04<br>-.06<br>Quantile treatment effects<br>-.08<br>-.1<br><!-- End of picture text --> Figure 7. Quantile treatment effect of minimum wage hikes on hourly wage rate _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _α_ 2 + _β_ 2 (left panel), and _α_ 2 (right panel) from from quantile treatment effect estimation of Equation (6) for hourly wages. Quantiles are indicated by the horizontal axis. Sample restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock.
B, but effectively these areas are defined by two criteria. First, they are blobs with contiguous pixels of the density of above 1,500 per sq. km. Then, for these blobs to be in our sample, they should have at least one UN listed metropolitan area and the populations of all the listed UN metropolitan areas in the blob should sum to at least 800,000. Once we have defined these areas, we then give the agglomeration the Landscan population number obtained by summing over all grid squares in the blob. The primary issue is that, with the lower density criterion, vast swathes of seemingly rural areas in India and China are combined into, and considered, gigantic urban areas regardless of whether the areas are really urban in nature. Hence, we prefer the higher density thresholds as well as a cross check with the official UN data.For 6 African countries, we did our own checks, but doing the world in detail for smaller places and densities was beyond our scope.

Given these criteria, we establish a set of 599 cities worldwide, with 451 in the developing world. We ran regressions with dependent variables, in logs, as follows: personal population density [PPD], simple population density [PD], the coefficient of variation term in eq (2), the De La Roca-Puga agglomeration measure [RPA], the simple average of the local De La Roca-Puga measure [AD], and the covariance term in (3). For the RPA measure, we use a spatial discount rate of -0.5, as compared to De La Roca and Puga (2017) who use no discounting. Later in the paper we will analyze the optimal rate of discount for a particular and narrower context, where we find that -0.5 is close to the optimal rate for Africa.

Figure 5 shows the differences in PPD worldwide by country; where within each country we take a weighted average of each city’s PPD. Blank areas are countries without cities in the data set. It is clear that African countries, in general, have very high PPD, as well as parts of South and East Asia. The question is whether that is just from high overall population density. We use simple regressions with dummies for regions of the world to answer that. Our regression results are presented in Tables 1a and 1b, where the top panel of each table gives the basic results controlling just for city ruggedness from Nunn and Puga (2012) and will represent what the raw data tell us. The bottom panel additionally controls for GDP per capita from the Penn World Tables (PWT 7.0), to see the extent to which differences in levels of development explain the patterns.

13
data (Bekaert and Harvey, 2003). While participation of foreign investors can increase the vulnerability of domestic markets to contagion from shocks in other countries as foreign investors re-organize their portfolios, evidence has shown that foreign investors are less likely to withdraw their money first in times of crisis and their trading activity has no destabilizing effect on their host countries’ capital markets.

Stock market reforms and capital account opening can have ambiguous effects on domestic stock markets. Allowing domestic firms to raise resources abroad, through ADRs or cross-listing on international stock exchanges allows these firms to raise additional resources. Recent research, however, has also shown a reduction in trading activity of these firms on domestic stock exchanges and a negative spill-over effect on the liquidity of other firms that do not go abroad (Levine and Schmukler, 2005). Similarly, international comparisons of the effect of capital market related reforms have shown a negative impact on trading on domestic markets as these reforms make domestic firms more attractive to international investors and thus allow them access to international markets (De la Torre, Gozzi Valdes and Schmukler, 2005). While this seems like a disappointing consequence of financial liberalization, policy makers have to remember that it is the access to financial services that matters not who provides the service or where it is provided.

Even more than equity flows, debt flows, especially short-term debt flows, can have an immediate effect on interest and exchange rates of emerging and industrial economies. While a liberalized financial system can function without a completely open capital account and thus liberalized cross-border debt flows, opening the capital account makes controls on domestic interest rates and credit flows difficult if not impossible to sustain and counterproductive and damaging, as large depositors and borrowers have the possibilities to circumvent them by investing and borrowing abroad. As capital account liberalization has exposed economies to substantial volatility in capital flows and macroeconomic prices, a consensus has developed to 26
areas uniformly, such as international conservation priorities and funding availability. As in all other analyses, we cluster standard errors at the level of protected area. The coefficient of interest is _β_ , which captures the change in _Y_ due to transference to AP management.

The matrix _Xit_ first includes an indicator for whether the protected area receives funding from the Global Environment Facility (GEF), which can influence scores if respondents believe future GEF funding depends on the scores they report. The matrix also includes indicators for who participated in responding to the questionnaire, as these identities can also influence scores reported. For example, participation from local community members could result in lower scores on average if these individuals tend to be less satisfied with protected area management than protected area managers themselves. Specifically, _Xit_ includes dummy variables for whether any of the following types of people were in the group that submitted the questionnaire: protected area managers, protected area staff, other protected area agency staff, NGO staff, members of the local community, donors, external experts, and other individuals. We also include indicators for whether any of these variables were missing, which allows us to retain these observations in our regressions.

The primary data from which we form the _Yit_ variables are responses to 30 questions [75, 76]. Valid answers range from 0 to 3. For example, for the third question, “Law enforcement”, respondents record 0 to represent “No effective capacity/resources”, 1 to indicate “There are major deficiencies in staff capacity/resources”, 2 for “The staff have acceptable capacity/resources”, and 3 for “The staff have excellent capacity/resources”. Consequently, we exclude responses with a value greater than 3 from our analyses. We adopt the categorization of other researchers in grouping the 30 questions into four dimensions: Design and Planning, Capacity and Resources, Monitoring and Enforcement Systems, and Decision-Making Inclusiveness [53, 76]. We calculate the mean response to questions in each category, resulting in four variables (one for each category). We standardize the category scores so that the regression coefficients are interpretable in terms of standard deviations.

15
measures of readiness are generic, including whether the firm has identified a foreign distribution agent, but others are sector-specific, having to do with conformity to quality regulation in the foreign market. The tool helps the agency to focus its efforts on where the social payoff is likely to be greatest. Another tool used is database for international buyers that allows them to browse the offerings of exporters.

_6) Use of private sector competition to deliver hard and soft infrastructure._ In Costa Rica, over 30 privately-owned export processing zones compete to cater to the needs of exporters and investors. Some of the zones provide plug-and-play office and production space, as well as schooling for the children of employees, and all offer renewable energy. By relying on the competitive market to deliver infrastructure, the investment and export promotion agencies have enriched their offering, without additional expenditure.

# **Conclusion**

Most discussion of industrial policy these days has focused on actions by the largest economies, like China and the United States, and a narrow set of industrial policy instruments, specifically tariffs and investment subsidies. This discussion is not relevant to governments in countries with small domestic markets pursuing export-led development strategies that require them to abide by international trade rules, and who lack fiscal space for large subsidy programs. Yet, these countries nonetheless need an approach to target policy instruments that resolve market failures specific to certain export sectors, and exploit latent opportunities for productive diversification. Such policy instruments can be called industrial policy because they require sector-specific information to implement, and are targeted at specific sectors. I have provided an outline of the approach many countries are taking and examples from a rich toolkit of policy instruments available, some of which have been validated by research in specific contexts.

The phrase developing countries used to be a euphemism for poor governance, some of which was linked to support of the wrong sectors or people. Even so, because lower-income countries are productive in fewer sectors and have less technology, targeting sectors can be more valuable for them. With recent improvements in governance and democratic accountability, there is cause for some optimism that the industrial policy of the future—with developing countries now licensed by economists to use it—can succeed. Even so, like all development policy, I 26