rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table A.30. The district fixed-effect RDD-DD regressions of households’ income using a poverty rate bandwidth of 7%</u>**

|||||Dependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|-0.0914|-0.5461|1.1514***|-0.4597*|1.1423|0.7776**|1.5803**|
||(0.1264)|(0.7667)|(0.3930)|(0.2445)|(0.9663)|(0.3817)|(0.6662)|
|(Poverty rate – 50) *Post-program|0.0086|-0.0587|-0.0836**|0.0089|0.0027|-0.0482|-0.0029|
|period|(0.0093)|(0.0469)|(0.0380)|(0.0231)|(0.0566)|(0.0334)|(0.0430)|
|Program * (Poverty rate – 50) *Post-|-0.0026|0.2659**|0.0081|0.0099|-0.3381|0.0885|-0.1123|
|program period|(0.0216)|(0.1325)|(0.0996)|(0.0436)|(0.2108)|(0.0904)|(0.1306)|
|Age of household heads|0.0311***|0.0417**|0.0473***|0.1476***|-0.0449***|-0.0808***|-0.0596***|
||(0.0022)|(0.0166)|(0.0094)|(0.0155)|(0.0117)|(0.0122)|(0.0119)|
|Age squared of household heads|-0.0003***|-0.0007***|-0.0006***|-0.0014***|0.0007***|0.0011***|0.0008***|
||(0.0000)|(0.0002)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0243|-0.0345|0.1911**|0.7423***|-0.4253***|-0.0480|-0.4027***|
|female=0)|(0.0203)|(0.1203)|(0.0726)|(0.0880)|(0.0598)|(0.0674)|(0.0793)|
|Ethnic minorities (yes=1, Kinh=0)|-0.5951***|-0.0827|-1.7646***|0.6101***|-0.5391***|0.6751***|-0.7962***|
||(0.0533)|(0.1938)|(0.1894)|(0.1033)|(0.1663)|(0.1386)|(0.1133)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.4848***|3.2073***|1.7711***|3.5179***|4.9014***|1.6246***|3.1781***|
||(0.0778)|(0.3741)|(0.2726)|(0.3957)|(0.3622)|(0.3822)|(0.2919)|
|Observations|26,803|26,803|26,803|26,803|26,803|26,803|26,803|
|R-squared|0.336|0.150|0.092|0.148|0.206|0.140|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

80
# **Table A 6 Differences on Key Individual, Household and Village Characteristics (Matched Sample)**<sup>**26**</sup>

|**Variables**|**Treatment**|**Control**|**T-Test**|
|---|---|---|---|
|Age of Head of Household|45.934|46.046|0.259|
|Education Level of Head of Household|3.824|3.916|0.599|
|Proportion of Educated Women to Total Members in Household|0.257|0.241|2.203**|
|Proportionof MarriedMembers toHouseholdMembers|0.480|0.498|2.081**|
|Log Time for Round-Trip to Fetch Drinking Water (Minutes)|2.653|2.687|1.219|
|Number of Household Members|4.807|4.421|6.130***|
|CrowdingIndex: RatioNumberof Members toDwellingRooms|2.251|2.322|1.572|
|Dependency Ratio Household|0.649|0.644|0.213|
|Total Number of Livestock Assets Owned 2012|4.503|1.908|2.122**|
|Total Numberof ProductiveAssets Owned2012|1.235|1.166|1.186|
|Total Number of Consumptive Assets Owned 2012|2.419|2.068|3.834***|
|SC or ST Household|0.791|0.784|0.557|
|FemaleHeadedHouseholds|0.115|0.092|2.169**|
|Household Type (Kutcha)-SECC 2013|0.567|0.549|1.073|
|Own Telephone Mobile Phone-SECC 2013|0.402|0.404|0.160|
|Own AnyLand-SECC2013|0.459|0.467|0.487|
|Own Irrigation Equipment-SECC 2013|0.062|0.062|0.072|
|Any Member has a Salaried Job-SECC 2013|0.053|0.061|0.614|
|Mainsource of income of Household-cultivation -SECC2013|0.398|0.399|0.075|
|Main source of income of Household-enterprises-SECC 2013|0.006|0.007|0.363|
|<br>Total Households-Census2011|192.322|250.679|4.977***|
|Total Population of Village-Census 2011|947.727|1,181.799|3.955***|
|Percentage of Female Population-Census 2011|49.811|49.581|3.265***|
|Worker force participation rate of all persons in the Village -<br>Census 2011|0.522|0.523|0.551|
|Worker force participation rate of females in the Village - Census<br>2011|0.489|0.494|0.937|
|Worker force participation rate of females in the Village - Census<br>2011|0.489|0.494|0.937|
|Total Numberof Micro-EnterprisesinVillage2012|14.043|19.894|1.487|
|Access to Pucca Road-Census 2011|0.788|0.796|0.638|
|Status of Agriculture Marketing Society-Census 2011|0.428|0.428|0.015|
|Percentage of IrrigatedLandfrom Total Land-SECC2013|5.894|6.502|1.166|
|Percentage of Households having Source of Light: Electricity -<br>Census2011|42.517|42.777|0.211|
|Percentage of Households having Bicycle-Census 2011|39.491|38.618|0.867|
|Density of Primary Health Centers (per 100 Households) -<br>Census 2011|0.013|0.007|2.207**|
|Density of Primary Schools (per 100Households)-Census2011|0.152|0.136|2.539**|
|Log of Total Geographical Area (Hectares)-Census 2011|6.006|6.040|1.141|
|Log Distance from Village of Nearest Bank (Kms.)|2.105|2.265|6.717***|
|LogDistrictHead Quarter(Distancein km)|3.915|3.907|0.344|
|Population Density ofthe Village-Census2011|2.652|2.536|0.967|

26 Significance levels: *:10%, **:5%, ***:1% 32
liquidity because of uncertainty) could have constrained lending to SMEs in the treated and untreated countries in the period after the global financial crisis.

In our sample of countries, we observe that Basel III was implemented dominantly by larger countries. Although they add to the explanatory power of the (first-stage) selection regression, other characteristics, such as foreign ownership of banks and membership in the G20 group of developing countries, do not appear significant at common statistical levels.

Table 14 shows the estimation results for our panel of firms. Here, we also find a negative effect of Basel III implementation on SME access to financing. In general, the panel results largely correspond to the cross-sectional results. The Basel III coefficient is negative but insignificant in columns (1) and (2), regression specifications that may still carry too much residual variation that is cleaned up by including country context and bank characteristics. In columns (3) and (4), which do so, the estimated effect is significantly negative—about half the size of the coefficient estimated for the cross-section data set. The panel regressions use matched, firm-bank data as well as firm– banking system data for firms that did not have credit or did not report the name of the bank.

Initially less financially included SMEs—those with bank accounts but without a bank loan (column (6) in table 14)—felt that their access to finance constraints worsened more than those of SMEs that were initially more financially included—those that had bank accounts and credit with a bank (column (5)). Interestingly, SMEs that were initially using bank credit (and having bank accounts) did not perceive a significant worsening in access to finance after Basel III implementation—although the estimated coefficient still bears a negative sign. We examine this result further by estimating a supplementary regression on a smaller sample of firms that initially used credit and can be matched to banks. The estimation results, reported in table A.13, in the appendix, provide some support for the insignificant effect of Basel III on firms that had access to credit before Basel III implementation.

The panel structure allows us to control for the initial conditions in access to finance. We find that the initial level of access to finance is an important determinant. This result broadly concurs with Ayyagari, Demirgüç-Kunt, and Maksimovic (2017), who find initial conditions to be critical for 31
# **1 Introduction**

A growing body of literature suggests that export participation leads to an increase in skill utilization within firms (Verhoogen 2008; Brambilla, Lederman and Porto, 2012). This relationship is consistent with several intuitive theoretical mechanisms. First, exporting may require expertise in international business and foreign languages (Matsuyama, 2007; Brambilla, Lederman and Porto, 2012). Second, it may induce firms to upgrade product quality, which is a skill intensive activity (Verhoogen, 2008; Bastos and Silva, 2010; Kugler and Verhoogen, 2012; Brambilla, Lederman and Porto, 2012; Bastos, Silva and Verhoogen, 2014). Third, by leading to an expansion in the scale of operations, entry in export markets may make it profitable for firms to pay the fixed costs associated with the adoption of more skill-intensive technologies (Yeaple, 2005; Bustos 2011).

While previous empirical research has documented a robust causal effect of export participation on skill intensity—as measured by workers’ schooling or occupation—, little is known about whether it leads to skill upgrading of the existing workforce through job training. Since skilled labor is a scarce factor and skill-acquisition is costly, training may be a relevant channel of adjustment to globalization, with potentially important implications for productivity and labor earnings. In this paper, we examine these hypotheses in a rich combination of administrative data for the Brazilian manufacturing sector, linking a worker-firm labor census with customs data on export status and detailed training records from the main provider.

The worker-firm data we use cover the universe of manufacturing firms formally registered in Brazil, and include longitudinal records on earnings and worker attributes, as well as firm characteristics. Using unique firm identifiers, we have linked these data with information from customs declarations indicating whether the firm had positive export revenues in each year. Using unique worker identifiers, we have matched these data with detailed administrative records on training activity at the trainee-level from the main provider in the manufacturing sector. The individual training records contain detailed information on training category and course duration, enrollment and completions dates. The training courses provided are grouped in five categories, which serve different objec2
# **Figure 1 (continued)**

## **Gross capital flows by country group (percent of trend GDP)**

## **(b) Emerging countries**

<!-- Start of picture text --> CIF<br>COD<br>1978 1983 1988 1993 1998 2003 2008 2013 2018<br>year<br>15 15<br>12 12<br>9 9<br>6 6<br>3 3<br>0 0<br>-3 -3<br><!-- End of picture text -->

## **(c) Developing countries**

<!-- Start of picture text --> CIF<br>COD<br>1978 1983 1988 1993 1998 2003 2008 2013 2018<br>year<br>12 12<br>9 9<br>6 6<br>3 3<br>0 0<br>-3 -3<br>-6 -6<br><!-- End of picture text --> 45
**Table 2: Summary of empirical research on supercycles**

|**Author(s)**|**Data**|**Main finding**|
|---|---|---|
|Cuddington and Jer-<br>rett (2008)|Six base metals, 1850-2006, de-<br>flated by the U.S. CPI and PPI|Four supercycles (the last was ongoing):<br>1890-1930, 1930-1962, 1962-1998, 1998-|
|Jerrett and Cudding-<br>ton (2008)|Three metals, 1850-2006, deflated<br>by the U.S. CPI|Three supercycles: 1850-1925, 1925-1998,<br>1998-|
|Cuddington and<br>Zellou (2012)|Crude oil, 1861-2010, deflated by<br>the U.S. CPI and PPI|Three supercycles after WWII which<br>comove with metals: 1861-1884, 1966-<br>1996, 1996-|
|Erten and Ocampo<br>(2013)|Prices of 24 commodities and indi-<br>ces, 1865-2010, deflated by the U.S.<br>CPI and MUV index|Four supercycles consistent with the<br>Prebisch-Singer hypothesis: 1890-1930,<br>1930-1965, 1970-1998, 1998-|
|Rossen (2015)|Prices of 20 metals, 1910-2011,<br>monthly, deflated by the U.S. CPI|Four supercycles: 1910-1930, 1930-1965,<br>1970-1998, 1998-|
|Erdem and Ünalmış<br>(2016)|Crude oil, 1861-2014, deflated by<br>the U.S. CPI|Three supercycles: 1861-1882, 1966-1996,<br>1996-|
|Buyuksahin, Mo, and<br>Zmitrowicz (2016)|Bank of Canada commodity index,<br>1899-2015, deflated by the U.S. PPI|Four supercycles: 1899-1932, 1933-1961,<br>1962-1995, 1996-|
|McGregor, Spinola,<br>and Verspagen (2018)|Five price indices, 1960-2016, de-<br>flated by the MUV|Two supercycles: 1960-1995, 1996-|
|Ojeda-Joya, Jaulin-<br>Mendez, and Bustos-<br>Pelaez (2019)|Index, 1865-2013 and 24 commodi-<br>ties, 1962-2010, deflated by the<br>MUV index|Supercycles are synchronized and de-<br>mand-driven:**_Oil_**: 1885-1950, 1965-1995,<br>1996-;**_Metals_**: 1877-1920, 1920-1945,<br>1945-1995, 1995-;**_Non-oil_**: 1895-1937,<br>1937-1996, 1996-|
|Jacks (2019)|Indices and 40 commodities, 1900-<br>2015, deflated by the U.S. CPI|Three or four medium-term cycles (de-<br>pending on the commodity) and mod-<br>estly increasing trend: 1903-1932, 1965-<br>1996, 1996-|
|Cordano and Zellou<br>(2020)|Natural gas prices, 1922-2015, de-<br>flated by the U.S. CPI|Three supercycles, strongly correlated<br>with oil supercycles: 1948-1970, 1970-<br>1994, 1994-2017|
|Erten and Ocampo<br>(2021)|Four indices as in Erten and<br>Ocampo (2013)|Four supercycles: 1890-1930, 1930-1965,<br>1970-1998, 1998-|
|This study|Six metal and nine agricultural<br>prices, 1900-2020, deflated by the<br>U.S. CPI|Four supercycles: 1904-1933, 1933-1966,<br>1966-1996, 1996-|

**Notes** : All papers (except Rossen 2015) use annual data and are based on the HP filter, except Erdem and Ünalmış (2016) who use BP and HP filters. The MUV index (Manufacturing Unit Value) is a measure of dollar-based global manufacturing inflation monitored by the World Bank.

_— 19 —_
# **C. Capital flows, recovery and growth**

South Asia has become much more financially integrated in the global economy over the last decade receiving higher capital inflows than ever before. With the onset of crisis, the euphoria of massive inflows witnessed by some economies of the region has given way to reversal of capital flows. With the ongoing global financial restructuring, it will take time for private foreign capital flows to recover. Even then the capital flows will be less accessible in a new risk-averse environment, and the cost of capital will be higher. How does this changed financial architecture going to affect recovery and medium term growth of South Asia?

In order to analyze this question we examine - how dependent is South Asia on these capital flows for growth? How vulnerable are the capital flows to South Asia?

## **_Domestic Savings driving Investments in South Asia_**

Before analyzing how dwindling capital inflows are going to affect the investment prospects in South Asia, we look at how South Asia was doing in terms of investment as compared to rest of the world prior to the crisis.

|**Table 4: Cro**<br>**Panel A**|**ss Countr**<br> <br>_Investm_|**y Compar**<br>_ent (% of_|**isons of Investment and S**<br>**Panel**|**avings**<br>**B**||
|---|---|---|---|---|---|
||_GD_|_P)_||_Savings (_|_% of GDP)_|
||1995|2006||1995|2006|
|log (real GDP per capita)|13.05***|7.92***|log (real GDP per capita)|16.03***|15.28*|
|log (real GDP per capita)^2|-0.80***|-0.49***|log (real GDP per capita)^2|-0.72***|-0.62|
|India|9.16***|13.67***|India|12.90***|13.7***|
|China|17.60***|22.93***|China|27.33***|26.64***|
|Bangladesh|2.73**|5.01***|Bangladesh|3.72*|4.94***|
|Pakistan|-1.37|-0.19|Pakistan|2.62|-2.91|
|Srilanka|4.37***|2.21***|Srilanka|1.55|-0.48|
|Control for Size|Yes|Yes|Control for Size|Yes|Yes|
|Observations|164|137|Observations|164|138|

_Notes_ : Robust standard errors are reported in parenthesis. *** represents significance at 1%, ** represents significance at 5%, * represents significance at 10%.Country size is measured by population.

We report the cross-country level regression on investment rate for 1995 and 2006, controlling for the level of per capita income, non-linearities in development (captured by the square of per capita income), and country size (proxied for by population) in Table 4, 23
- 24 -

products' labor intensity ratio differ from the United States average. The table shows the number and per cent of labor intensive products falling in each broad industry group and also classifies these items in a range of "very high" to "moderately" labor intensive. 17/ Among the 19 industry groups listed, the highest concentration of labor intensive products is now in textile mill products (25 products or 13 per cent of all four-digit SIC items) and apparel and textiles (31 products or 17 per cent of all labor intensive items). Furthermore, the table shows that these products are entirely coacentrated in the "very high" labor intensity group (cotton mills, artificial fiber mills, men's suits and children's outerwear), or are distributed between the "very high" and "high" groups. Appendix Table 1 shows that these products (identified in terms of their corresponding SITC group) were also among the most highly labor intensive goods in the mid-1960s.

Aside from textiles and clothing, wood products (SIC 24) have the third highest concentration of labor intensive goods (17 items or 9 per cent of the total) while 7 per cent of the labor intensive products fall in SIC 34 (fabricated metals) and SIC 39 (miscellaneous manufactures). Analysis of the underlying product data (see Appendix Table 6) show various furniture products

- 17/ In Table 6, a four-digit SIC product is classified labor intensive if its factor proportions index is 10 points or more below the United States average. This "cut-off" point is lower than that used by the NBER due to the mixed export results developing countries had for products closer to the United States average. Furthermore, products are considered to be "very highly" labor intensive if their ratios are 40 points or more below the U.S. average, while items with a "high" labor intensity are those with ratios between 0.60 and 0.74. "Moderate" labor intensive products are those with factor proportions ratios between 0.75 and 0.90. Appendix Table 6 lists the four-digit SIC labor intensive products selected using these criteria and also provides related shipment and employment statistics.
However, the literature studying the impacts of groundwater irrigation on agricultural outcomes is scant. In one study in Mali, Burney, Woltering, Burke, Naylor, and Pasternak (2010) find the introduction of solar-powered irrigation pumps is associated with a 1.9 ton increase in the production of crops, which results in a 17% reduction in food insecurity in treatment villages. The scarcity of research is attributable to low rates of groundwaterirrigation adoption in the region. We contribute to filling this gap in the literature by simulating the potential gains in agricultural production through groundwater irrigation across the region.

This study makes three important contributions.

First, we create a novel database for SSA that combines agricultural data with a granularity of 291,798 grids with aquifer data composed of 8,099 grids. This approach allows us to combine information on agricultural production, productivity, and climatic and soil conditions with information on groundwater availability for each agricultural grid.

Second, we provide quantitative evidence on agricultural productivity gains associated with groundwater access. Our simulation framework estimates the gains in agricultural production through groundwater irrigation on existing rain-fed croplands. We reach these estimates by determining the proportion of existing crop water deficits in agricultural grids that can be mitigated through groundwater irrigation. We find the median gains in production range between 27.97% and 129.42% depending on the crop of interest and methodology used to predict crop yield gains.

Third, this is the first study to incorporate sustainability conditions on groundwater extraction. We do so by simulating production gains only in agriculture grids with a sufficiently low ratio of groundwater extraction to recharge. Furthermore, our analysis is confined to calculating gains solely from renewable groundwater sources within the region.

The remainder of this paper is organized as follows. Section 2 provides a brief background on the need for groundwater irrigation in SSA. Section 3 details the data used for the simulation. Section 4 provides statistics on groundwater availability in the region and the current state of agriculture. Section 5 describes the methodology used for the simulation. Section 6 presents and discusses the results. Section 7 conducts several robustness checks. Section 8 discusses groundwater access costs, and section 9 concludes.

# **2 Background**

The majority of the population in SSA resides in rural areas, where the primary source of livelihood is rain-fed agriculture. Extreme weather conditions such as droughts, high temperatures, or delayed rainfall can impose severe hardships, especially for the rural population in agriculture-dependent areas (Khan, Kuate, Pongou, & Zhang, 2024). These hardships are further exacerbated by the lack of adaptive measures to climate change.

However, SSA harbors a largely untapped reserve of groundwater aquifers, which could mitigate the effects of adverse rainfall events such as droughts. A report suggests 3
_Table 13: The effect of socioemotional skills, conditional on applicant CV clicked_

||Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|SE skills|0.007|-0.027*|-0.027*|-0.026|-0.051*|
||(0.010)|(0.016)|(0.016)|(0.016)|(0.029)|
|Signaled SE skill required in vacancy||0.004|0.004|0.001|0.001|
|||(0.022)|(0.022)|(0.022)|(0.043)|
|SE skills * Signaled SE skill required in vacancy||0.049**|0.050**|0.049**|0.092**|
|||(0.020)|(0.020)|(0.020)|(0.036)|
|SE skills * Acct|||||-0.010|
||||||(0.042)|
|SE skills * Mrkt|||||0.036|
||||||(0.044)|
|SE skills * IT|||||0.076*|
||||||(0.045)|
|Signaled SE skill required in vacancy * Acct|||||0.035|
||||||(0.061)|
|Signaled SE skill required in vacancy * Mrkt|||||-0.002|
||||||(0.061)|
|Signaled SE skill required in vacancy * IT|||||-0.030|
||||||(0.062)|
|SE skills * Signaled SE skill required in vacancy * Acct|||||-0.055<br>(0.054)|
|SE skills * Signaled SE skill required in vacancy * Mrkt|||||-0.052<br>(0.054)|
|SE skills * Signaled SE skill required in vacancy * IT|||||-0.080|
||||||(0.056)|
|Age|||-0.0460***|-0.0439***|-0.0433***|
||||(0.0118)|(0.0116)|(0.0117)|
|Experience (in months)|||0.0015|0.0012|0.0012|
||||(0.0013)|(0.0013)|(0.0013)|
|Objective beauty|||0.0190|0.0181|0.0180|
||||(0.0274)|(0.0273)|(0.0275)|
|Subjective beauty|||0.0043|0.0081|0.0072|
||||(0.0128)|(0.0126)|(0.0126)|
|Worked in one firm only|||-0.0004|-0.0025|-0.0022|
||||(0.0140)|(0.0139)|(0.0139)|
|Constant|0.172***|0.169***|<br>1.291***|<br>1.241***|<br>1.280***|
||(0.010)|(0.018)|(0.294)|(0.291)|(0.296)|
|Vacancychar.|No|No|No|Yes|Yes|
|N.obs.|3469|3469|3469|3469|3469|
|R-squared|0.000|0.002|0.007|0.017|0.019|

**Notes:** Models 1 to 6 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _SE skills_ takes on the value 1 for treatment CVs, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Vacancy characteristics include total number of applications for the vacancy, occupational clusters and location. Symbols ∗ ∗ ∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.