rafmacalaba/data-use-mentions-extended huggingface dataset:
receiving a higher wage.<sup>6</sup> Both output and the observed wage are therefore correlated with the unobserved "sophistication" of the production technology.

Following Anderson and Hsiao (1982), we therefore difference the data to eliminate **Iii,** yielding our base specification Unless the idiosyncratic error followed a random walk, this differencing necessarily gives _Alit_ = _aAlA(t_ - **I)** + _Awitq(9(r), r)_ + _VAqt_ + AS + A4it the transformed error an MA(+) structure that is correlated with the differenced LDV. This can be overcome by using lags of greater than t-4 as instruments. We follow Arellano and Bond 's (1991) employment of additional lags as instruments to improve the efficiency of the estimates in a Generalized Method of Moments (GMM) context.

We instrument lagged differenced employment with its lag and with second and further lags of capital stocks or output. Though we attempted to instrument for any remaining endogeneity in wages, the results, as with Roberts and Skoufias and Maloney and Ribeiro, were poor and counterintuitive and in the end we do not instrument for them. As we instrument lagged differenced employment with its lag, we lose three years of data in each panel. This is not so much of a problem in Colombia where the sample begins substantially before the change in regime. In Mexico we lose 1984-86, the period when most reforms were occurring. However, the full impact of these changes was probably not instantaneous and the exchange rate appreciated sharply across the period so the experiment is still of interest. The Chilean data now begins in 1982, the last year of the

> 6 This does not imply lack of competitiveness in product markets. Firms take the wage for workers of all combinations of characteristics they desire. Since we do not observe these characteristics, the firm may

15
positive, statistically significant, and larger than for OECD countries. In the case of India, the coefficient is positive but not statistically significant. Results for innovation-induced sales are mixed. While in the case of India, the coefficient is positive and statistically significant, the results are not significant for Nepal, and show a negative sign for Bangladesh.

Finally, one additional important finding is that the degree of novelty does not introduce any additional effect on productivity, and the returns are the same as for imitation. Thus, the evidence suggests that there are positive returns to imitation in South Asia, mostly coming from very incremental innovations in Bangladesh and India. But radical innovations do not increase firm performance above what imitation can make a firm gain. The results discussed so far are robust to alternative methodologies.<sup>11</sup> <u>Table 24 Innovation and productivity estimates in Nepal</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log(L)|0.3769***|0.3586***|0.5314***|0.5333***|
||(0.065)|(0.065)|(0.054)|(0.054)|
|Log(K/L)|0.2369***|0.2421***|0.1816***|0.1816***|
||(0.045)|(0.046)|(0.045)|(0.045)|
|Prod &/or process|1.3959***|1.5707***|||
||(0.339)|(0.297)|||
|Prod&proc*national||0.
OIC countries. We also included the developing countries in our analysis as another benchmark to see whether the OIC countries are lagging behind the average developing economies on a global level in terms of financial inclusion enhancement.

Below, we discuss main gaps in current implementation of _Shariah_ -compliant microfinance, SME, and micro-insurance which are hindering their effectiveness to enhance the financial inclusion.

# **IV.1. Gaps in Financial Inclusion – Microfinance (MF)**

# **Strong demand for Islamic microfinance services in OIC countries is not met by the**

**supply.** An IFC-commissioned market studies in the MENA region suggest that between 20 and 60 percent of interviewed microenterprises and low income individuals indicated a preference for _Shariah_ -compliant products. For some, the lack of _Shariah_ -compliant products is an absolute constraint to financial access, while for others, this is a preference and they continue to use conventional financial services in the absence of competitive Islamic ones.<sup>27</sup> This gap is supported by statistics in

**Figure 3** , which suggests that despite the fact that OIC countries have more MF deposits and accounts per thousand adults, values of MFI deposits and loans as percentage of GDP are still much lower in OIC countries (0.61% and 0.79%) compared with developing countries (0.78% and 0.97%) and low income countries (0.92% and 1.19%).

# **Figure 3 Limited scale of Microfinance deposits and loans**

<!-- Start of picture text --> 68 1.19%<br>62<br>0.97%<br>0.92%<br>48<br>44 OIC countries 0.78% 0.79% OIC countries<br>35 0.61%<br>30<br>Developing Developing<br>countries countries<br>Low‐income Low‐income
<!-- Start of picture text --> Total Household Income and Revenues Proxy (Nominal USD), Month<br>oO8 97 ~~-~-- UP Control Non-UP<br>©<br>lo} 61ee64<br>S OTT Sse<br>i ne<br>¢ 7<br>°<br>N<br>°<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Any Adult Ever Skipped or Cut Size of Meals in Past Month<br>o Percentage of Households<br>= —----UP Control ——— Non-UP<br>83<br>4 oe<br>ae 68<br>B55 ==<br>t+ 33<br>fo}g<br>oO<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Index of Per Capita Food Consumption Proxy<br>Control UP Households at EndlineH == 1, Constant<br>w+ 198<br>2<br>---=- UP Control ——— Non-UP<br>©<br>=4 1.36<br>TTS _ 1.30<br>N Tass >fsL<br>= ie<br>- “77>...1.00~<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Any Child Ever Skipped or Cut Size of Meals in Past Month<br>o Percentage of Households<br>= -----UP Control ——— Non-UP<br>3 76<br>Uoeeoc7<br>8 Ueno 57<br>oOt+ _-7<br>ay
Figure 12. Poverty and structural reforms <!-- Start of picture text --> A. Poverty rates, by strength of institutions<br>Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Lowest Highest<br>Regulatory quality<br><!-- End of picture text --> <!-- Start of picture text --> B. Poverty rates, by financial inclusion<br>Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Lowest Highest<br>Frequency of account ownership<br><!-- End of picture text --> <!-- Start of picture text --> C. Poverty rates, by Doing Business ranking D. Poverty rates, by trade openness<br>Percent of population Percent of total poor Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40 40 Poverty Rate Share of total poor (RHS) 40<br>30 30 30 30<br>20 20 20 20<br>10 10 10 10<br>0 0 0 0<br>Lowest Highest Lowest Highest<br>Ease of Doing Business Trade across borders<br><!-- End of picture text --> Source: World Bank Note: The poverty rate is an unweighted average in each group. Share of population is the cumulative total. Based on poverty data for 2015.

A “Highest” indicates quartile of EMDEs with the strongest regulatory quality (based on data for year with latest poverty data). “Lowest” indicates quartile of EMDEs with the weakest regulatory quality. The
_Figure 12: Vulnerability rate by decile_ <!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>poorest 2 3 4 5 6 7 8 9 richest<br><!-- End of picture text --> Source: Authors’ calculations based on quarterly HBS.

The vulnerability measure allows identifying the most vulnerable groups of population (see Annex, Table A4 for details). Those residing in small cities and rural areas have average vulnerability levels of 27.0 and 37.5 percent correspondingly. During the crisis vulnerability levels in those areas surpassed 40 percent. Despite low pre‐crisis vulnerability levels, residents of large cities also saw their vulnerability increase to over 30 percent of that group. Vulnerability is naturally high for those partially employed or non‐employed, and it increased to the 50 percent level during the peak of the crisis. Similar vulnerability levels and dynamics can be observed among individuals with low levels of education. Across the age distribution, children are the most vulnerable group, with average vulnerability of 51.3 percent; and going as high as 74.3 percent at the peak of the crisis. Accordingly, households with children, especially households with two children, households with three and more children, and households with children and a single adult also have high vulnerability levels – 42.3, 89.7 and 65.1 percent, going up to 60.7, 90.6 and 72.7 percent correspondingly during the crisis. Retirees and retiree households do not fall into the vulnerable category as the minimal pension is above the official poverty line.

Following Lopez‐Calva and Ortiz‐Juarez (2013), we also estimate the vulnerability threshold in disposable income. We estimate the following regression (see Table A17 for full
from 5 to 40 years (based only on historical frequency) and map rural areas where more than 30 and 50 percent of cropland or grassland were affected in any growing season.

# 3.3 HEAT

We use global extreme heat hazard data from Ridder et al. (2017) prepared for the World Bank.<sup>12</sup> The hazard is categorized using the simplified Wet Bulb Globe Temperature indicator representing temperature, humidity, and radiative impacts. The WBGT is derived from global daily maximum air temperature and dew point temperatures for a 30-year period (1981-2010) from the ERA-Interim Global archive and topographic data from the Global Multi-resolution Terrain Elevation Data 2010. These 10-km resolutions yearly maxima of 3-day mean maximal WBGTs are then used to construct the probabilistic hazard intensity maps for a given return period. We utilize the 5, 20 and 100-year return period maps and define locations exposed to heatwave if the annual maxima of 3-day mean maximal WBGTs exceeds 32, 33, 34, 35 and 36°C.

# 3.4 CYCLONE

We use a global synthetic tropical cyclone dataset from Bloemendaal, Haigh, de Moel et al. (2020), generated using a synthetic resampling algorithm called STORM (Synthetic Tropical cyclOne geneRation Model). STORM is applied to 38 years of historical cyclone track data from the International Best Track Archive for Climate Stewardship (IBTrACS) to statistically extend the dataset to 10,000 years of cyclone activity. The data has been validated against historical observations and previous studies (Bloemendaal,

> 11 The drought hazard maps are available from <u>https://www.fao.org/giews/earthobservation/access.jsp. Refer to Van Hoolst et</u> al. (2016) for more information on the method of constructing ASI. More information on the Global Administrative Unit Layers (GAUL) is available at https://data.apps.fao.org/map/catalog/static/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654. At GAUL level 2, there
It combines the georeferenced data on quality of roads in Armenia collected by the World Bank, matching the geographic coordinates of household cluster locations collected by the DHS. The combined dataset with geographic locations provides an opportunity to calculate the proximity to good quality roads from

> 13Road quality categories: Very good (IRI range between 1.0 and 2.0), Good (2.0 - 4.0), Fair (4.0 - 6.0), Poor (6.0 - 10.0), Very poor (10.0 - 16.0).

> 14The questionnaires were slightly modified over the period of 10 years, therefore, the variables have been adjusted accordingly to ensure comparability over the years.

13
. Section 2 describes the data and empirical strategy used in this analysis. Section 3 presents the main results demonstrating the impact of nitrogen fertilizer on agricultural productivity, and pollution in the waterways along with the overall impact of subsidies. Finally, Section 4 concludes with policy implications and discusses the way forward.

# 2. Data and Empirical Strategy

To study the impact of nitrogen fertilizer on agricultural productivity, the paper uses a grid cell-level data set covering the entire world. For the analysis, the land area is split into grid cells measuring 0.5 degrees on each side, which is approximately 56 × 56 kilometers at the equator. The sample period extends from 2000 to 2013. The following equation is estimated at the global scale:

> 10 Due to the heavy focus on urea, studies have shown that the Mali subsidy program failed to address the more limiting factor for crop growth, which in some cases was phosphorus rather than nitrogen (Smale and Thériault, 2018). Similarly, a Tanzania field study that conducted soil testing found that sulfur was deficient in all the plots and remained a critical limiting factor for growth in maize yields. Yet, national fertilizer recommendations mandated by the government did not include sulfur (Harou et al., 2021).

> 11 Fertilizers are essential for plant growth but beyond a point, adding more fertilizer may not be able to boost yield.

> 12 For this reason, the fallout of nitrogen pollution is considered one of the most important environmental issues of the twentyfirst century (Kanter 2018). Recent estimates suggest that nitrogen may be the world’s largest global externality, even surpassing carbon (Keeler et al. 2016).

4
. 1._** _Methods_

Five rounds of the Household Expenditure Surveys of the BBS are used, corresponding to the years 1983/84, 1985/86, 1988/89, 1991/92, and 1995/96. The five rounds cover from 3,840 to 7,240 households per round. Poverty lines are used not only for computing poverty measures, but also as price deflators for estimating growth and inequality in real per capita consumption, which is preferred to income as an indicator of standards of living. The methodology used for estimating the regional lower (identifying the very poor) and upper (identifying the poor) poverty lines is described in Wodon (1997).
Figure 6: Premature deaths. Based on the concentration-response function of Deryugina et al. (AER, 2019) for adults 65 or older <!-- Start of picture text --> 2000 2005 2010 2015 2020<br>Year<br>15000<br>10000<br>Premature deaths<br>5000<br><!-- End of picture text --> _Notes:_ This figure plots the evolution of drought-induced premature deaths in LAC. Details of the calculation of premature deaths are presented in Section 4.1. The spread of the box plot displayed each year results from the uncertainty in our estimation of excess PM2 _._ 5 concentrations and the value of the observed FHD. Dots represent outliers.

To descriptively assess in our setting whether the population residing near power plants tends to be worse off than the overall population, we use down-scaled Human Development Index (HDI) data from Sherman et al. (2023) to compute power plant level mean HDI relative to country-level HDI. As illustrated in Figure 7, in nearly four of every five combustion power plants, the population residing near the plant has a considerably lower relative level of HDI. Moreover, this pattern is systematic across all types of combustion power plants (i.e., coal, gas, oil, biomass). This finding is consistent with previous work on air quality and inequality, which has highlighted the correlation between poverty and exposure to PM2 _._ 5 both across and within countries (e.g., Rentschler and Leonova, 2023; Jbaily et al., 2022). The association of lower HDI levels near combustion power plants can be a result of socioeconomic sorting into cheaper housing near industrial zones, siting decisions to place plants in areas with less political power, often coinciding with low-income 20
target. This paper is able to signicantly improve on this strategy by utilizing a new source of data to track population 3As will be described in more detail in the next section, the long incubation period for malaria allows people to travel without knowing they are infected. Additionally, those in high malaria settings typically develop immunity and do not experience malaria symptoms yet can infect mosquitoes when they travel to low malaria settings.

> 4In Senegal, the main ocial source of data on population movement is census data that only includes long-term migration statistics every 10 years. There are other surveys that ask about commuting, such as the Household Mobility Survey of Dakar (EMTASUD), but it is only for one point in time, it is focused only on Dakar, and it was done in 2000 and in 2015).

2
(q10c), Lack_Trust (q10d), Lack_Money (q10e), Religious (q10f), Family_Has (q10g) The Life in Transition Survey (LITS, 2010)

- **Bank Account Ownership** (Bank_Account_h): Ownership of a bank account by any household member

- Original variable name in dataset: q225c  Dummy variable equals 1 if any individual in the household has a bank account, equals 0 if no one has an account.

- **Savings Rate** (Savings_h): Any left over money to put into savings in the household in a typical month.

- Original variable name in dataset: q223_t1 Dummy variable equals 1 if household has saved money, equals 0 if household has not.

- **Debit Card Ownership** (Debit_Card_h): Ownership of a debit card by any household member Original variable name in dataset: q225d  Dummy variable equals 1 if any individual in the household has a debit card, equals 0 if no one has a a card.

- **Credit Card Ownership** (Credit_Card_h): Ownership of a credit card by any household number Original variable name in dataset: q225e Dummy variable equals 1 if any individual in the household has a credit card, equals 0 if no one has a card.

- **Borrowed Money** (Borrow): Money borrowed from anyone (e.g. friend, other person or institution) by any individual in the household.

- Original variable name in dataset: q805 Dummy variable equals 1 if any individual in the household has borrowed money, equals 0 if no one has borrowed

24
**Table 11: Robustness tests – Relationship between the employment rate of older persons (55-64) and the labor market outcomes of youth (15-24)**

||(1)<br>Youth|(2)<br>Youth|(3)<br>Youth|(4)<br>Youth|(5)<br>Youth|
|---|---|---|---|---|---|
||Employment|Unemployment|Hours of work(log)|Hourlywage(log)|Monthlyincome(log)|
|Generalized least squares||||||
|Employment rate of old persons|0.492***|-0.509***|-0.002**|-0.001|-0.004|
|(55-64)|(0.058)|(0.055)|(0.001)|(0.003)|(0.003)|
|Lagged independent variable||||||
|Lag of employment rate of old|0.406***|-0.491***|-0.001|-0.003|-0.005*|
|persons(T-2) (55-64)|(0.071)|(0.073)|(0.001)|(0.003)|(0.003)|

Source: Author’s calculations using Sakernas data Notes: Province-level control variables included are the log of hourly income of youth (for models 1-3), the unemployment rate (for models (3-5), the poverty rate, share of employment in the agriculture sector, share of employment in the industrial sector, share of youth, share of population with a primary education or less, log of GRP per capita (constant 2010 IDR), and growth in GRP per capita .
0.482|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level structural transformation and school enrollment when SOE reform is controlled for. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita in both panels and average years of schooling in panel A. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 4.4 Migration

Migration creates another major threat to the validity of our findings. FDI constitutes a local labor market shock and workers can respond to such shocks by moving across cities. When a city receives huge manufacturing FDI inflows, labor demand in industry and related services sectors will rise, lifting wage rates in the city and attracting workers from other places. More and better work opportunities also attract students to this city for college education. Migration thus magnifies the effect of FDI on local structural transformation and school enrollment. On the other hand, migration could reduce school enrollment in poorer areas as youths drop out of school to work in richer cities (De Brauw and Giles 2017). Fortunately, migration should not affect our results too much because China adopts a strict Hukou system that limits the free movement of people across locations, and the share of migrants indeed remained relatively low during our study period. The population census records individual’s Hukou address, current residence address and where the individual lived 5 years before the census date. We can therefore identify individuals that migrated to the current location within five years. While the number of cross-county migrants increased dramatically from 18 million to 76 million during 1990-2005, these migrants only accounted for a
(1) of Table 2 might have partially captured the negative correlation between human capital and LDO restrictions. The interaction e¤ect has the right sign. Both of the coe¢ cients are statistically highly signi...cant (at 1 percent or lower level). As discussed before, a negative e¤ect of LDO restrictions in this speci...cation can be interpreted as strong evidence, as the estimated e¤ect is likely to be significantly biased towards zero. The evidence from this speci...cation that both the direct e¤ect of LDO restrictions, and its interaction with travel time are statistically signi...cant at 1 percent level

> 23 Note that we do not control for population in a DSD in any of the regressions. Because historical data on DSD level population are not available from the period before LDO restrictions were imposed. Since the main channel through which the land restrictions a¤ect wage in our general equilibrium model is migration and e¤ective labor endowment, any variable capturing e¤ective labor endowment is thus a ‘bad control’in the terminology of Angrist and Pischke (2009).

> 24 The omitted category for the ethnicity (religion) dummies is Sinhalese. About 84 percent of Sri Lanka’s population are Sinhalese.

21
. The resulting estimates provide a large efficiency gain compared with direct survey estimates.

We mainly consider Empirical Best Predictor (EBP) models, which have a long history in small area estimation and can accommodate subarea-level auxiliary data to produce estimates of poverty rates and their mean squared error for target areas. EBP models differ from the “ELL method” (Elbers, Lanjouw, and Lanjouw 2003), that has traditionally been used for poverty mapping by the World Bank because they incorporate additional information from the survey by conditioning the area effect on survey residuals.<sup>5</sup> This can lead to substantial efficiency gains when the variation in welfare or poverty contributed by the sample is large, relative to the variation contributed by the auxiliary data. On the other hand, a drawback of EBP models is the required assumption that the stochastic error terms in the model follow a normal distribution. While this normality assumption can be tested by examining the residuals from the prediction model, violating the assumption will introduce bias into the estimates.

In the traditional case when the auxiliary data are household-level data from a census, the sample is a small fraction of the size of the census and EBP models typically lead to minor efficiency gains.<sup>6</sup> In these cases, it is not obvious that EBP models are preferred to simulations based on standard random effect models that do not condition the area effect on the sample but allow for departures from normality. When the auxiliary data are only available at the subarea level, however, the effective size and statistical power of the auxiliary data are much less than that of a full household census. The share of variation contributed by the sample therefore becomes larger, and as shown in results presented below, EBP

> 4 Battese, Harter, and Fuller (1988), Erciulescu et al (2019).

> 5 For example, the methods described in Elbers Lanjouw and Lanjouw (2003) and Tarozzi and Deaton (2009) are not EBP models.

> 6