rafmacalaba/datause-encoder-data huggingface dataset:
for facilitating firm labor productivity growth. _Second_ , though we observe important heterogeneity of the BE effects, we do find several key elements to be critically important in most contexts: access to bank finance, modern infrastructure, and agglomeration (especially capacity agglomeration and being located in a big city). Thus, it is important to add the agglomeration environment to the list of key BE ingredients. This category has emerged as a key determinant of firm growth across countries, perhaps as important as many of the traditional BE elements that we emphasize such as human capital, institutions, access to finance, and so on; moreover, it is a key element that facilitates productivity growth in low-income countries, thus allowing them to catch up.

# **References**

Angrist, Jushua, and Jorn-Steffen Pischke. 2009. _Mostly Harmless Econometrics_ . Princeton: Princeton University Press.

- Acemoglu, Daron. 2010. “Theory, General Equilibrium, and Political Economy in Developing Economics.” _Journal of Economic Perspective_ 24(3), 17-32.

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Kilic, T., E. Whitney, and P. Winters. 2015. “Decentralised Beneficiary Targeting in Large-Scale Development Programmes: Insights from the Malawi Farm Input Subsidy Programme.” _Journal of African Economies_ 24 (1): 26–56.

Kling, R., J. B. Liebman, and L. F. Katz. 2007. “Experimental Analysis of Neighborhood Effects.” _Econometrica_ 75:83-119.

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Mason, N. M., and M. Smale. 2013. “Impacts of Subsidized Hybrid Seed on Indicators of Economic Well-Being Among Smallholder Maize Growers in Zambia.” _Agricultural Economics_ 44 (6): 659–70.

Mason, N. M., and T. S. Jayne. 2013. “Fertilizer subsidies and smallholder commercial fertilizer purchases: Crowding out, leakage, and policy implications for Zambia.” _Journal of Agricultural Economics_ 64 (3): 558-62.

Mason, N. M, T. S. Jayne, and N. van de Walle. 2017. “The Political Economy of Fertilizer Subsidy Programs in Africa: Evidence from Zambia” _American Journal of Agricultural Economics_ 99(3): 705– 731.

Mather, D., and T. S. Jayne. 2015. “Fertilizer subsidies and the role of targeting in crowding out: An assessment of smallholder fertilizer demand in Kenya.” Selected paper for the 29th International Conference of Agricultural Economists, Milan, Italy, August 8-14.

Morris, M., V. Kelly, R. Kopicki, and D. Byerlee. 2007. _Fertilizer Use in African Agriculture: Lessons Learned and Good Practice Guidelines_ . Washington, D.C.: World Bank.

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Ricker-Gilbert, J., T. S. Jayne, and E. Chirwa. 2011. “Subsidies and crowding out: A double-hurdle model of fertilizer demand in Malawi.” _American Journal of Agricultural Economics_ 93 (1): 26-42.

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Vanlauwe, B., J. Kihara, P. Chivenge, P. Pypers, R. Coe, and J. Six. 2011. “Agronomic use efficiency of N fertilizer in maize-based systems in sub-Saharan Africa within the context of integrated soil fertility management.” _Plant and Soil_ 339 (1-2): 35-50.

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samples for which different types of predictors are selected when implementing LASSO to select variables for the EBP model. Predictors are grouped into eight categories: Weather and climate variables, Vegetation (specifically the Normalized Difference Vegetation Index), Land Cover Classification (LC), modeled population estimates, pollution indicators, distance to different locations, and latitude and longitude (GPS). The results are shown in Table 10.

When predicting assets, Pollution, Distance, and Land Cover are selected most frequently, more than 96 percent of the time. Weather and vegetation are also quite common, at 90 and 88 percent, respectively. GPS and population are less common, as they are only selected 58 and 52 percent of the time, respectively. Patterns are fairly similar for poverty prediction. Vegetation, Land cover, and distance to major cities are also frequently selected. Weather-related variables, however, are only selected in 65 percent of the samples and the GPS indicators (latitude and longitude) are almost never selected.

Selection by LASSO is a coarse measure of the importance of predictor variables. We therefore perform Shapley decompositions of the predictors in one sample per country and indicator using the XGBoost results, following Lundberg et al. (2019). For each sub-area, this procedure calculates the average of the absolute value of changes in the prediction of assets or poverty due to each predictor variable, taken over all possible orderings of the predictors in the tree. These average Shapley values are then averaged across all sub-areas in the sample. The variables with the largest Shapley values for each country and indicator are listed in Table 11 .

In nearly all cases, the landcover shares (coverfraction) stand out as key variables in the XGBoost models. These include the fraction of the sub-areas classified as urban, shrub, grass, bare, trees, crops, and water. On average these variables and their higher-level aggregates account for almost half of the top 10 variables for asset prediction and 60 percent of the top 10 variables for poverty prediction. Pollution variables such as ozone (o3) and nitrogen dixode (no2) also appear in several cases, as do vegetation (ndvi), population (pop), weather (starting with weather) and distance to cities (variables starting with distto). In general, the listed variables tend to be correlated with population density, which is in turn correlated with asset ownership and poverty.

35
probability of failing after 10 years. Water schemes with shallower groundwater—between 0 and 25 meters below ground—have more than a 30% probability of failing in their first year and almost a 60% probability after 10 years.

# _(b) Empirical Results_

The logit regression estimates of the determinants of the non-functionality of water schemes are presented in Table 2 for the full sample of water schemes that are one-year-old, three to six years old, and more than eight years old. The logit regression and Shapley decomposition estimates were run on 2,808 water schemes (Table 2), those that are one-year-old, three to six years old, or older than eight years. The results for water schemes that are two, seven, and eight years old are not included, but can be furnished upon request. The analysis also excludes the 13 water schemes that are missing from the groundwater productivity and depth estimates. As in the case of the LWSS curves, the outcome variable is binary and equals 0 if the water scheme is functional and 1 if it is non-functional.<sup>10</sup> The results indicate that the presence of a WASHCOM has a negative and significant impact on the non-functionality of all water schemes at the 1% level, for three-to-six-year-old schemes at the 5% level, and for eight-year-old schemes at the 10% level. The availability of spare parts is negative and significant for non-functionality at the 1% level for the full sample and for three-to-six-year-old schemes, and at the 5% level for one-year-old schemes and for those more than eight years old. Together, these results suggest that the presence of a WASHCOM and the availability of spare parts play a major role in preventing the breakdown of water schemes. In terms of promoters, using donors as the base-level, community/philanthropist promoters had a negative and significant impact at the 10% level for all water schemes and for three-to-six-year-old schemes. For all water schemes, using the north east as the base level zone, the chances of failure were high and significant at the 1% level in the southeast and southcentral regions. In the south-central region, the chances were also high and significant at the 1% level for

> 10 <mark>For the following four variables: Repairing Agents, Groundwater Productivity, Groundwater Storage and Depth to Groundwater, we collapsed some of these categories together in the functionality curves for ease of interpretability in the graphs but decided to include the un-collapsed versions in the demographic table (Table 2) and the regression table (Table 3).</mark>

15
# **Table A3 Cross‐sectional identification**

|||**La**|**bor productivi**|**ty (3‐year cha**|**nge)**||
|---|---|---|---|---|---|---|
|**Quartile**|**1998**|**2001**|**2004**|**2007**|**2009**|**2011**|
|HGF Downstream share|0.031***|0.014|0.038***|0.017**|0.080***|0.025***|
||(3.211)|(1.465)|(4.564)|(1.964)|(7.043)|(2.760)|
|HGF Upstream share|0.045***|0.022**|‐0.001|0.019**|0.014|0.001|
||(4.741)|(2.516)|(‐0.067)|(2.444)|(1.517)|(0.115)|
|HGF Intra‐industry share|0.008|‐0.000|0.019**|0.003|‐0.000|0.027***|
||(0.982)|(‐0.008)|(2.266)|(0.513)|(‐0.025)|(3.248)|
|Region FE|yes|yes|yes|yes|yes|yes|
|Sector FE|yes|yes|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|yes|yes|
|Observations|16,420|20,497|26,473|26,647|24,833|25,724|
|R‐squared|0.240|0.185|0.251|0.188|0.159|0.160|
||||**TFP (3‐ye**|**ar change)**|||
|**Quartile**|**1998**|**2001**|**2004**|**2007**|**2009**|**2011**|
|HGF Downstream share|0.056***|‐0.007|0.037***|0.049***|0.078***|0.039***|
||(3.135)|(‐0.586)|(3.757)|(4.254)|(6.413)|(3.663)|
|HGF Upstream share|0.023|0.023*|‐0.000|0.014|0.013|‐0.005|
||(1.307)|(1.871)|(‐0.011)|(1.335)|(1.269)|(‐0.434)|
|HGF Intra‐industry share|0.027**|0.022**|0.024***|0.024**|0.002|0.021**|
||(2.136)|(2.190)|(2.605)|(2.440)|(0.214)|(2.489)|
|Region FE|yes|yes|yes|yes|yes|yes|
|Sector FE|yes|yes|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|yes|yes|
|Observations|16,420|20,497|26,473|26,647|24,833|25,724|
|R‐squared|0.158|0.143|0.215|0.141|0.142|0.143|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level.

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18 (15) Ln _Y_ = 5.805 + 0.351 _S_ , R<sup>2</sup> = 0.62, N = 128 ( _t_ =14.4) Variants of the above Mincer-macro function using per capita income as the dependent variable in cross-country data have been fitted by Krueger and Lindahl (2001), Heckman and Klenow (1997) and Hanushek and Woessmann (2008). Our estimate of a 35 percent ―return‖ to one additional year of schooling is nearly identical to theirs. After fitting such a function, Heckman and Klenow (1997) concluded that: _“each additional year of schooling attainment in a country is associated with about 30% higher GDP per capita, whether one looks at 1990, 1985 or 1960..... The macro-Mincer coefficients....are consistent with large positive external productivity gains to economy-wide schooling attainment.”_ Barro and Lee (2010), among many others, have fitted the same function using output per worker (rather than per capita income) as the dependent variable and adding a measure of physical capital stock as an independent variable. Since their estimate of the _b_ coefficient is about one-half of ours, we adopted a 50 percent reduction of our estimated welfare loss as a lower bound. Table 7 and Figure 8 present estimates the welfare loss in terms of potentially higher per capita income had Pakistan a higher level of educational attainment as indicated in Col. 2, Table 7.

**Table 7.  Pakistan: Welfare loss** **<u>as percent of per capita income</u>**

|Year<br>(1)|Enhanced<br>years of<br>schooling<br>(2)|Per capit<br>(constan<br>Actual<br>(3)|a income<br>t 1990 $ )<br>Simulated<br>(4)|Welfa<br>(<br>Lower<br>bound<br>(5)|re loss<br>%)<br>Upper<br>bound<br>(6)|
|---|---|---|---|---|---|
|1900|0.2|264|354|17.1|34.2|
|1950|3.0|643|947|23.6|47.2|
|2010|6.0|2,239|2,714|10.6|21.2|
|2050|8.0|4,570|5,475|9.9|19.8|

Source: Based on equation (15), Table 6 and assumed enhanced years of schooling Notes: Col. 2, hypothetical years of schooling used in the simulation Col. 3, Actual or projected per capita income given the 1950-2010 growth rate Col. 4, Estimate value based on equation (15) and Col. 2 Col. 5 = (Col. 6) / 2 Col. 6 = ( (Col. 4 – Col. 3) / Col 3)100
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# **S7 Table. Intervention effects on diarrhea and length-for-age z score, sex-by-intervention interaction models**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Diarrhea -<br>Simple|Diarrhea -<br>Interaction|Height for<br>Age -<br>Simple|Height for<br>Age -<br>Interaction|
|Treatment|-0.010|-0.004|-0.014|0.071|
||(0.022)|(0.027)|(0.070)|(0.098)|
|Child's sex|0.010|0.014|0.134**|0.206**|
||(0.014)|(0.018)|(0.062)|(0.084)|
|Child's age (years)|-0.043***|-0.043***|-0.233***|-0.233***|
||(0.004)|(0.004)|(0.033)|(0.033)|
|Kasai Province # cluster_strata=2|0.267***|0.267***|-0.085|-0.089|
||(0.050)|(0.050)|(0.200)|(0.199)|
|Kasai Province # cluster_strata=3|0.232***|0.232***|0.222|0.213|
||(0.078)|(0.078)|(0.172)|(0.174)|
|Kasai Central Province # cluster_strata=2|0.372***|0.372***|-0.347|-0.351|
||(0.059)|(0.058)|(0.261)|(0.260)|
|Kasai Central Province # cluster_strata=3|0.335***|0.335***|-0.143|-0.150|
||(0.063)|(0.063)|(0.168)|(0.167)|
|Sud-Kivu Province # cluster_strata=2|0.273***|0.273***|-0.729***|-0.737***|
||(0.041)|(0.041)|(0.161)|(0.159)|
|Sud-Kivu Province # cluster_strata=3|0.268***|0.268***|-0.240|-0.242|
||(0.042)|(0.042)|(0.164)|(0.163)|
|Treatment # Female||-0.011||-0.168|
|||(0.028)||(0.122)|
|Constant|0.235***|0.233***|-1.323***|-1.354***|
||(0.039)|(0.040)|(0.182)|(0.185)|
|Observations|4072|4072|2142|2142|
|Wald_F||0.159||1.883|
|Wald_p||0.691||0.173|

Coefficients and standard errors from linear models of the primary outcomes on intervention group, fixed effects for randomization strata, fixed effects for child sex, and intervention-by-sex interaction terms. Standard errors are clustered by cluster (group of villages). There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units. The Wald F is a test statistic for the null hypothesis that all of the interaction term coefficients are zero. * p<.1, ** p<.05, *** p<.01"
medium-sized private companies heavily rely on bank financing, with negligible (if any) opportunities in corporate bond and stock markets.

Other potential distortions in the incentives facing financial institutions that invest in government debt. First, banks are more likely to prefer a short-term portfolio allocation, thus raising rollover risk for the government. Second, domestic banks and institutional investors may be induced by moral suasion to absorb excessive public debt, which may amplify the deleterious effect of a debt crisis in case the government is following unsustainable policies (Panizza, 2008). Third, a large bank exposure to government securities could undermine the solvency of financial institutions in times of economic distress, potentially leading to a systematic banking crisis (Diouf and Dufrense, 2012). Distortions also arise when it is the Central Bank that finances the government’s short-term cash imbalances through overdraft facilities for managing daily transactions and cover unexpected shortfalls in revenue (Johnson, 2001). A higher independence of the Central Bank helps lowering the leverage of the government in borrowing though these facilities.

For the _Debt Structure Sample_ in 1996-2011, Figure 5 shows the participation of investors holding the domestic public debt. On average, the banking system comprising commercial banks and the Central Bank holds nearly three-quarters of the domestic liabilities, with a quite stable participation. Within the banking system, the share of commercial banks has increased since the early 2000s. The breakdown into HIPCs and non-HIPCs reveals that the former rely much more on Central Bank lending (e.g., advances) whereas the latter tap commercial banks and other market investors.

**<u>Figure 5: Domestic Debt by Holder.</u>**

<!-- Start of picture text --> Domestic debt composition by holders. HIPCs Domestic debt composition by holders. Non-HIPCs<br>Central Bank Commercial Bank Central Bank Commercial Bank<br>Others Others<br>1996199719981999200020012002200320042005200620072008200920102011 1996199719981999200020012002200320042005200620072008200920102011<br>100 100<br>80 80<br>60 60<br>40 40<br>Domestic Debt by holders 20 Domestic Debt by holders 20<br>0 0<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset._

# **4.6. Relationships between cost of domestic debt, maturity, and investor base**

Using the _Debt Structure Short Sample_ , which can be seen as a constellation of domestic public debt portfolios for 36 countries in recent years, the casual inspection of simple correlations provides preliminary evidence on the relationships between cost of domestic debt, maturity, and investor base.

17
water shortages in a month experienced by a firm can lead to annual average losses of about 14.5 percent of the monthly sales per worker of the average informal firm in the sample that uses water for business activities.

To summarize, the study contributes to the literature in the following ways: (a) it is the first study to establish and quantify the effect of poor water infrastructure quality on the productivity of informal firms, and (b) the study uses a rich data set that covers over 2,400 informal firms, of which 710 use water for business activities, and the data are comparable across 12 economies. Section 2 introduces the data, section 3 provides the empirical specification, section 4 presents the results and section 5 concludes.

# **2. Data**

The main data source of the analysis is the surveys of informal firms, defined as unregistered firms, conducted by the World Bank Enterprise surveys unit (henceforth called Informal Enterprise Surveys). The data encompass about 2,400 informal firms over a cross-section of 12 economies across Africa, East Asia, and Latin America between 2009 and 2014. About 710 firms use water for business activities and water shortage data are only available for these firms. The sample of analysis is these 710 firms unless otherwise indicated. Table A1 (in the appendix) presents the list of countries with the total duration of water shortages in days over a month. The highest figure is from Ghana with an average of 6.75 days of water shortages over a month. The lowest is Burkina Faso with 0.01 days. For the full sample, on a typical month the average sales per worker for a firm is about US$1,173 without about 2 to 3 employees. About 55 percent of the firms in the sample manufacture their own product. Unfortunately a larger number of the surveys do not have detailed information on the activity of the informal firms. For 8 countries in the sample, such 6
**<u>Table 1: Financial Indicators in Nigeria</u>**

||Liquid<br>liabilities|Deposit Money<br>Bankassets|Private<br>Credit|Financial system<br>deposits|Number of<br>banks|
|---|---|---|---|---|---|
|1985|27.04%|25.78%|14.79%|20.52%|40|
|1990|19.51%|11.59%|11.88%|13.73%|107|
|1991|21.08%|11.24%|11.45%|15.11%|119|
|1992|19.87%|11.11%|11.64%|14.29%|119|
|1993|25.01%|16.05%|13.88%|17.37%|119|
|1994|25.88%|17.50%|12.39%|16.43%|116|
|1995|15.98%|11.42%|9.18%|9.80%|115|
|1996|12.86%|9.99%|8.42%|8.12%|115|
|1997|13.83%|11.57%|9.69%|9.07%|115|
|1998|16.71%|13.71%|11.83%|11.12%|89|
|1999|18.47%|16.23%|12.25%|12.93%|89|
|2000|18.44%|16.33%|10.96%|13.31%|89|
|2001|25.14%|20.46%|14.89%|18.07%|89|
|2002|26.78%|22.36%|16.10%|19.33%|89|
|2003|24.78%|20.84%|14.60%|17.31%|89|
|2004|24.59%|21.40%|15.47%|17.33%|89|

Source: Updated version of the Financial Structure Database (World Bank) by Beck et al (2000) and number of banks by Lewis and Stein (2002) and own data. Liquid liabilities are over GDP. Bank assets are claims by deposit money banks on the domestic real nonfinancial sector as a share of GDP. Private credit is by deposit money banks and other financial institutions to GDP. Financial system deposits are defined as demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP.

22
|**Panel (b): Odisha**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||||I|nput<br>Livelihood|||Mobility<br>Health|||
||Employed|Durables|Tuition|s|Politics|Store|Centre|Friend|Bank|
|ST|0.187***|-0.0166|-0.0354|-0.0610|-0.0613|0.0493|0.0256|0.0165|-0.0453|
||(4.81)|(-0.45)|(-0.67)|(-1.19)|(-1.24)|(0.97)|(1.16)|(1.03)|(-1.03)|
|OBC|-0.0511***|-0.0899***|0.0017|-0.0864***|0.0460|-0.0346|-0.006|0.0078|0.0277|
||(-3.31)|(-4.15)|(0.06)|(-3.34)|(1.66)|(-1.28)|(-0.41)|(0.60)|(1.18)|
|Muslim|0.101|0.119|0.148|-0.0703|0.0135|-0.00933|0.00558|0.0164|-0.0318|
||(1.69)|(1.58)|(1.61)|(-0.77)|(0.14)|(-0.10)|(0.11)|(1.30)|(-0.47)|
|FC|-0.0859***|-0.0364|0.0173|-0.0572*|0.0335|-0.0521|0.0135|0.0110|0.0403|
||(-5.63)|(-1.59)|(0.61)|(-2.10)|(1.16)|(-1.87)|(0.98)|(0.81)|(1.60)|
|Some Schooling|-0.0726***|0.0630**|0.0629**|0.0237|0.00656|-0.0751**|0.00636|0.0023|0.117***|
||(-4.57)|(3.24)|(2.69)|(1.02)|(0.27)|(-3.17)|(0.51)|(0.23)|(6.06)|
|Female headed household|0.0767**|0.0206|-0.0164|-0.0616|0.188**|0.105|0.0270|0.0193|-0.0534|
||(2.67)|(0.37)|(-0.26)|(-0.96)|(2.75)|(1.58)|(0.88)|(0.56)|(-1.00)|
|Per capita expenditure|-0.0087|-0.0007|0.0065|0.0147|-0.0067|-0.008|0.0046|0.0031|-0.0031|
||(-1.77)|(-0.09)|(0.80)|(1.76)|(-0.86)|(-0.94)|(1.04)|(1.46)|(-0.38)|
|Per capita expenditure||||||||-||
|squared|0.0004*|0.0001|-0.0001|-0.0005|0.0003|0.0005|-0.00009|0.0000|0.0003|
||(2.10)|(0.20)|(-0.44)|(-1.50)|(0.91)|(1.35)|(-0.63)|(-0.14)<br>0.0054|(0.90)|
|Land|-0.0087*|0.0088|0.0034|-0.0141|0.0033|-0.0328***|0.0029|*|-0.0090|
||(-2.09)|(1.64)|(0.40)|(-1.76)|(0.39)|(-3.88)|(0.93)|(2.29)|(-1.36)|
|Observations|4077|2246|2246|2246|2246|2246|2246|2246|2246|
|Adjusted R-squared|0.212|0.141|0.188|0.199|0.201|0.244|0.075|0.063|0.088|

Notes: (1) Source: Author's calculations based on data collected by Social Observatory, World Bank and Government of Odisha. Notes (2) - (5) of Table 3(a) apply.

28