rafmacalaba/datause-encoder-data huggingface dataset:
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel B: Regulatory Framework and Practices (0 100)</u>

|||Dep var: % of W|omen borrowers<br>||
|---|---|---|---|---|
||Version 1 (|<br>Ful samples)|Version 2 (Dro<br>sam|<br>pping Rural bank<br>ples)|
||OLS|Robust Reg|OLS|Robust Reg|
||(1)|(2)|(3)|(4)|
|Regulatory Framework and Practices (0-100)|-0.0040**|-0.0009|-0.0045***|-0.0006|
||[0.041]|[0.324]|[0.007]|[0.446]|
|NBFI dummy * Regulatory Framework and Practices (0-100)|-0.0007|-0.0016|-0.0001|-0.001|
||[0.737]|[0.247]|[0.975]|[0.420]|
|Combined group dummy * Regulatory Framework and Practices (0-100)|-0.0028|-0.0033**|0.0007|0.0004|
||[0.360]|[0.020]|[0.717]|[0.802]|
|NBFI dummy|-0.0469|0.0215|-0.068|0.0085|
||[0.698]|[0.784]|[0.565]|[0.903]|
|Combined group dummy|-0.0395|-0.0127|-0.1563|-0.1226|
||[0.797]|[0.877]|[0.129]|[0.178]|
|Observations|611|611|557|557|
|Number of countries|39|39|39|39|
|R-squared|0.449|0.523|0.46|0.577|
|Adjusted R-squared|0.431|0.507|0.441|0.562|
|_Microscope index+ interaction term (P-value in brackets)_|||||
|NBFI dummy|-0.00477|-0.00255**|-0.00461|-0.00164|
||[0.107]|[0.033]|[0.125]|[0.122]|
|Combined group dummy|-0.00681**|-0.00426***|-0.00382|-0.000234|
||[0.032]|[0.001]|[0.156]|[0.879]|

34
than older ones, but since the education level of the young is not much higher than that of the old, average education is not expected to increase.<sup>9</sup> Figure 3: Demographic transition by region <!-- Start of picture text --> a) ECA, LAC, SSA, and High-income b) EAP, MENA, and South Asia<br>Population share ratio Population share ratio<br>between age groups 20−24 and 60−64 between age groups 20−24 and 60−64<br>Europe & Central Asia East Asia & Pacific<br>Latin America & Caribbean Middle East & North Africa<br>Sub−Saharan Africa South Asia<br>High Income<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>6 5.5<br>5<br>5<br>Ratio 4 Ratio 4.5<br>3<br>4<br>2 3.5<br><!-- End of picture text --> Source: Authors elaboration based on Barro and Lee (2013) An interesting region is LAC, where countries are beginning their aging process as the intergenerational demographic ratio shows a kind of inverted U in Figure 2, but this is compensated by the rapidly increasing educational gap. A similar pattern is observed for EAP, with a decisively stronger aging trend. Figure 2 shows that the other regions – MENA, Sub-Saharan Africa, South Asia – are in earlier phases of the demographic transition. Their increasing demographic ratios will boost the impact of the intergenerational education gap and push up average education level strongly. population will be shaped by a younger and more educated cohort coming from developing countries, especially regions that are lagging behind on economic growth and per capita income level.

Figures 2 and 3 can be extended to the future using population projections and some simple assumptions on education of future young cohort. Indeed, going forward, the proportion of skilled workers will increase about 43.7% in low- and middle-income (LMI) countries between 2015 and 2050; it will decline approximately 3.3% in high-income countries during the same period. Moreover, the share of the population from LMI countries was approximately 82.7% in 2015. By 2050 they will represent about 85.7% of total population.<sup>10</sup> Figure 1 in the introduction summarizes how the increasing in the number of workers with more than nine years of schooling will be predominantly driven by countries in LMI regions.

The forthcoming wave of changes in the global labor market will, therefore, come from the combination and growing intensity of the demographic and education transitions just described.

> 9 Ahmed et. al. (2016) provides further description of different trends of demographic change across the word.

> 10 United Nations (2015), medium variant scenario.

9
obtain shapefiles with land use typologies from the municipal government, which allows us to approximately classify floor space in each location as either commercial or residential. We further obtain the Amman zoning restriction rules, which assign to each building type the percentage of the plot that can be used for construction and the maximum number of floors that can be constructed (among other constraints not modeled here). We merge the land use typology of each plot to the relevant building categories from the zoning rules to calculate the maximum floor space that can legally be developed in each location. We perform this step separately for commercial and residential building types.

**Travel Times with Road Travel.** To compute average travel times between all locations, we use the road network for Amman and assign average travel speeds to different types of road segments based on estimates from the World Bank Transportation Unit. Specifically, we assign a speed of 35 km/hour for primary roads, 20 km/h for secondary roads, 10 km/h for tertiary roads, 8 km/h for other road types, and 5 km/h for walking. For metro lines we assign a speed of 35 km/h and for monorail 45 km/h. In the counterfactual, we compute commuting times with the new BRT routes to which we assign a traveling speed of 25 km/h.

Figure 4 shows average commuting times for each residency neighborhood to all possible work locations where we weigh travel times of each work destination by the number of jobs available in this destination. Aligned with the previous results on employment density, we see that central neighborhoods are best connected to jobs while the outskirts have larger average commuting costs to available employment opportunities. Commuting times are particularly large for the south and some of the most western and northern neighborhoods.

# **5 Calibration Results**

9
**2. Consumption subsidy in water and sanitation services**<sup>**3**</sup>

In the late 1980s, water rates were on average less than half of what was needed to finance provision of the service, with prices covering less than 20% of outlays in regions with higher production costs. Tthis form of subsidy was highly inefficient (users were not confronted with the real cost of water services), and the benefits were greater among highincome families who consumed larger amounts. Prior to privatization, however, charges had to be raised so that the water companies could cover their costs. Unlike what happened with the other public services, the process that should have led to the privatization of the water companies was only started in the final stages of the military regime (1973-1989), and it could not be concluded before democracy was restored. The need to raise charges acted as a significant hindrance to the rapid transfer of sanitation companies to the private sector, as privatization accompanied by a substantial price hike would have been politically unpopular.

Sectoral modernization had begun in 1977, with the creation of the _Servicio Nacional de Obras Sanitarias_ (Sendos) at the Ministry of Public Works. This service absorbed several agencies belonging to different ministries and made it possible to downsize the workforce from 10,000 to 3,000. Apart from regulatory responsibilities for the whole sector, Sendos was put in charge of providing water services in the regions. In the same year, water companies were set up in the Santiago Metropolitan Region (Emos) and in the Vth Region (Esval), based on pre-existing small companies. Emos and Esval were also related to the State through the Ministry of Public Works. The creation of Sendos represented a significant change of approach. Until then, the financing of the service was

> 3 This section builds on Blanlot (1999) and Mideplan (1999).

7
Self-reported food price inflation is also negatively associated with consumption per capita. Although model (3) of Table 2 does not fully remove transfers from consumption per capita, we choose to use this specification for the simulations to benefit from the use of the three rounds of the IHS.

# **3.2 Vulnerability Estimates**

Building on the consumption model presented in Table 2, we predict consumption in 4,000 states of the world. This simulation is then used to calculate the vulnerability measures discussed earlier. The vulnerability estimates, along with the static poverty indicators, are presented in Table 5.<sup>15</sup> The static poverty figures are not identical to the official poverty statistics since the underlying consumption aggregate does not include cash transfer and food aid. They are relatively close to the official statistics in 2010/11 (IHS 3) since households received less assistance in that relatively affluent year but are higher in 2016/17 (IHS 4) when several households experienced drought.<sup>16</sup> Malawi experienced a severe drought before the 2016/17 survey. Therefore, it is expected that the recorded poverty in this year would likely be higher than the vulnerability to poverty. The 2004/05 round was preceded by a growing season comparable to the historical average. As expected, the recorded moderate headcount poverty is comparable to the share of the population with more than 50 percent chance of falling below the moderate poverty line. After the major drought in 2016, the vulnerability rate (52 percent) was lower than the moderate poverty rate (53.7 percent) recorded in IHS 4. In other words, the magnitude of drought in 2016 was so large that the chance of falling below the poverty line as a result of an even higher magnitude shock was low. On the other hand, during a good weather year such as 2010/11, the vulnerability rate (53.8 percent) was higher than the static poverty rates recorded in that year (51 percent).

The expected poverty gap and severity of poverty are always higher than their static counterparts. The poverty gap accounts for the number of poor as well as how far their consumption is from the poverty line. The severity of poverty gives more weight to the households who are the furthest from the poverty line. In our simulations, the predicted consumption of the poor and ultra-poor individuals is consistently lower than the actual/static consumption, likely due to their higher probability to suffer from idiosyncratic shocks. Therefore, expected poverty gap and expected severity of poverty are overall higher

> 15 We also present results disaggregated by region and urban/rural characteristics in the annex. Results are available upon request.

> 16 In 2010/11, the official moderate poverty headcount was 50.7 percent (17.3 percent for urban and 56.6 percent for rural), while the poverty rate in 2016/17 was 51.5 percent (17.7 percent for urban and 59.5 percent for rural).

22
are largely localized serving farmers within the immediate vicinity of the mills (Macchiavello and Morjaria, 2015). This is largely because raw coffee is bulky and the travel cost associated with hauling coffee from farms to the mills, particularly given the topography of Rwanda, increases with distance. Thus, after a given distance threshold, the net benefits from hauling coffee to these mills decrease with distance. Therefore, for our baseline analysis, we choose a conservative distance threshold of 2.5km,<sup>8</sup> as the optimal size of the catchment area. To put this into perspective, we classify students living within a 2.5km radius of a coffee mill as treated while students living beyond the 2.5km buffer as control. We also present alternative estimations and show that our results are fairly robust even if we extend the size of the catchment area up to 5km from a mill. Figure 4 provides a graphical representation of our treatment and control locations. Further, in Figure 8 we estimate the effects of the coffee mills across distance splines using a spatial lag model to validate the choice of our baseline distance for defining treatment and control areas.

It is noteworthy to emphasize that we proxy a student’s place of residence (residence) by the location of the school, due to the lack of data on students’ residency and place of birth. Admittedly, this is a strong assumption, but given the wide geographic footprints of primary schools in Rwanda, as shown in Figure 3, school location is a reasonable approximation of students’ residence.<sup>9</sup> As a result, _φ_<sup>ˆ</sup> , is essentially an intent-to-treat (ITT) estimate of the medium-term effect of exposure to the coffee mills in early life on student test scores.

Further, equation 1 implicitly assumes that the impact of the opening of coffee mills on the cognitive development of children is largely present during the time of birth. Hence, only children born during or after the opening of the coffee mills are likely to be beneficiaries of these positive impacts. However, when a mill starts operating in a community, it continues and does not shut down. Thus, children born say 1-2 years before the opening may potentially benefit from the income shocks associated with the opening of the coffee mills in the community, thus improving their cognitive development and helping them perform well in school. To this end, in Section 5.3.1, we present additional results by replacing our 0/1 treatment variable with a continuous treatment measure, measuring the share of years between ages 0-5 that a student was exposed to coffee mills. Reassuringly, both measures yield similar conclusions: early-life exposure to coffee mills has a positive impact on student performance.

# _Event Study_

Finally, the validity of our research design rests on the assumption of (plausibly) exogenous

> 8plausibly the median size of the catchment areas

> 9Most primary school students in Rwanda live within a 2km radius of their school. See https://blogs. worldbank.org/nasikiliza/accelerating-afe-rwandas-development-midst-overlapping-crises

9
which will also simultaneously affect training intensity and the size distribution in the economy. Therefore, we are confident that our results are not driven simply by the sector composition within countries or to the cross country variation in institutions. Rather with our reduced form, our findings will be driven by comparing differences across firms in the training intensity within the same country and detailed (3-digit ISIC) sector of activity. This decision reflects the fact that both firm size and the intensity to train (as well as the several dimensions of the business environment that we will explore in next section) have substantial variation within countries and sectors. Moreover, controlling for a set of interaction dummies by country and sector also accounts for potential omitted variables and possible measurement errors across countries.

# 4. **Empirical Findings**

# 4.1. **Main Results**

Table 3 reports the point estimates for the different variables in equation (2). The specifications across different columns differ in the controls included. In column (1) we just control for firm size and country dummies, in column (2) we add a proxy for the degree of firm openness (dummy variable for whether firm exports and has some foreign owned capital), age of the firm, public ownership and share of skilled workers in the workforce. Column (3) includes in addition to the variables in column (2) a proxy for technological innovation and column (4) includes country-city fixed effects.<sup>16</sup> The findings in column (1) show that there is a statistically significant and quantitatively important positive correlation between the investment in job training and firm size. In particular, small, medium and large firms are 17.1, 37.8 and 49.8 percentage points more likely to invest in job training than micro firms, respectively. These differences are slightly reduced, although remain quantitatively important and statistically significant, as across columns, we include additional control variables, like firm observable characteristics in columns (2) and (3) and country-city and sector unobservable characteristics, in column (4).

> 16 The _Enterprise Surveys_ report information on whether the firm is located in the capital city or in cities with more than 1 million workers. The country-city fixed effects include in column (4) of table 3 are the interaction of this city dummy with the country dummies. This specification also controls for sector 3 digit fixed effects.

15
5 long as six or seven years (as mentioned above), in the longer run the price indices appear to have a common trend. A com^on trend is even more pronounced in the data for the rand zone (Figure lb)

# 2.1 Principal Component **Analysis of the CFA zone**

In order to examine the degree of price level convergence in the CFA zone more closely, the annual data for the (log-)CPI of France and of the nine CFA countries for which this was available, was examined for the period 1964-8713. The hypothesis to be considered is that most of the variation in these countries' price levels can be summarised by a single common factor. To this end the principal components of the price data were computed" 4 . The striking result is that the first principal compon.ent explains 99.3% of the total variation in this data set. The bulk of inflation in the CFA countries and France is essentially a single common variable. The second principal component accounts for a further 0.3%, while the third can only manage just over 0.1%. At most, the first two 15 components can be of interest.

Figure 2 plots actual CPI for most of the countries together with the first two principal components. Each panel shows a country's CPI against the same background of thp two principal components. As can be seen, the first principal component tracks actual CPI rather closely: in fact the correlation between rhe first component and the CPI is better than 0.992 for each country. The loading of each country on the first component does 13 Note: figures for Senegal 1964-66 were extrapolated backwards. All of the data used in the paper is from _Internatlonal Financial Statistics_ with the exception of the average lending rates in Lesotho (Section 4 below) which were kindly provided by the Central Bank of Lesotho.

14 Principal components analysis is a technique used when it is thought - possible to summarize most of the variation in a large number of variables in this case the CPIs for the ten countries - by just a few artificially constructed variables. Loosely speaking, the first principal component is an artificial variable constructed so that it has the highest possible correlation with the collection of variables being examined; the second principal component has the highest correlation wIth the residuals left after subtracting out what is explained by the first principal component. The "loading' of a particular country on the first principal component is the regression coefficient obtained by regressing the country's CPI on that component.

15 A number of standard tests reject the hypothesis that all roots but the first are zero, hence, despite its small explnatory power, it seems reasonable to examine briefly the loadings on the second component. The first principal component of the differenced price series explains 88.2 per cent of total variation in that series, with a further 4.9 per cent and 4.0 per cent being explained by the second and third components.
Sivadasan (2009) uses a dataset that is similar to ours for the early years of the reforms (1986-1994) and finds that the reduction in final goods tariffs and the FDI liberalization increased productivity. He also documents that these reforms were linked with average productivity increases, but not reallocation, in the early 1990’s. Nataraj (2010) compares the reactions of the organized and unorganized manufacturing sectors to trade liberalization, and finds that while the reduction in final goods tariffs increased productivity significantly in the unorganized sector, the reduction in input tariffs was more important for productivity growth in the organized sector. Aghion, Burgess, Redding and Zilibotti (2008) find that following the removal of licensing requirements, the number of factories and output in the organized sector increased, particularly in states with relatively less restrictive labor regulations.

Our study contributes to these two strands of literature in several ways. First, we document that market-share reallocations were important to overall productivity growth immediately following the start of the 1991 reforms, while average productivity gains were more important during the periods from 1985-1990 and 1998-2004. Second, we show that the trade and FDI liberalizations explain a substantial amount of overall productivity growth. Third, we construct a panel that represents a large share of the organized manufacturing sector and spans the period from 1985 to 2004. We use the panel to explore a new decomposition method that disentangles the contributions of surviving, entering and exiting firms to overall productivity growth. We illustrate that this method can provide a richer set of results, but that the results are very sensitive to the correct treatment of exit. Our findings indicate that researchers working with panel datasets in which entry and exit are not well-identified should be cautious when applying this type of decomposition. Finally, we also use the panel to confirm that the trade and FDI reforms contributed substantially to average, within-firm productivity growth.

The rest of this paper is organized as follows. Section 2 provides a brief background on the Indian reforms; Section 3 describes the data and outlines the construction of the panel of firms; Section 4 discusses the empirical framework and presents results; and Section 5 concludes.

5