rafmacalaba/datause-encoder-data huggingface dataset:
Table A10: Balance on baseline characteristics of observations included in the tax record and firm survey samples

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Variable|Court-|In ...|Diference|Obser-|
||only|sample|(2)-(1)|vations|
|PANEL A: Tax record sample (party level)|||||
|Entered after decree application|0.427<br>(0.495)|0.433<br>(0.496)|-0.004<br>(0.016)|5,401|
|Party is plaintif|0.471|0.504|0.024|5,401|
||(0.499)|(0.500)|(0.023)||
|Claim amount (in million FCFA)|103.881<br>(496.240)|80.125<br>(318.276)|-25.645*<br>(14.690)|3,926|
|Above-median claim amount|0.600<br>|0.594<br>|-0.028*<br>|3,926|
||(0.490)|(0.491)|(0.015)||
|2012 revenues (IHS transformation)|19.435<br>(7.791)|22.079<br>(2.830)|2.627***<br>(0.256)|3,785|
|PANEL B: Tax record sample (frm level)|||||
|Share of frm’s cases that entered after decree|0.416<br>(0.461)|0.420<br>(0.412)|-0.000<br>(0.022)|2,154|
|Share of frm’s cases that are as plaintif|0.439<br>(0.467)|0.528<br>(0.418)|0.092***<br>(0.021)|2,154|
|Average claim amount of cases frm is involved in|128.828<br>(605.822)|90.312<br>(318.627)|-39.248<br>(28.337)|1,740|
|2012 revenues (IHS transformation)|13.983<br>(9.070)|20.826<br>(3.005)|6.938***<br>(0.446)|993|
|Number of cases|2.293<br>(7.534)|3.088<br>(4.622)|0.759***<br>(0.292)|2,154|
|PANEL C: Firm survey sample (frm level)|||||
|Share of frm’s cases that entered after decree|0.417<br>(0.451)|0.415<br>(0.428)|-0.001<br>(0.026)|2,154|
|Share of frm’s cases that are as plaintif|0.458<br>(0.459)|0.498<br>(0.432)|0.040<br>(0.027)|2,154|
|Average claim amount of cases frm is involved in|121.896<br>(558.669)|92.522<br>(408.148)|-26.819<br>(34.326)|1,740|
|2012 revenues (IHS transformation)|17.642<br>(7.444)|18.939<br>(5.979)|1.312**<br>(0.508)|993|
|Number of cases|2.385|3.339|0.966*|2,154|
||(6.461)|(9.194)|(0.533)||

<u>Notes:</u> Balance on baseline characteristics, Panel A: comparing case parties across court-only and tax record sample; Panel B: comparing court-only firms with those that have at least one party in the tax record sample; Panel C: comparing firms across court-only and firm survey sample. Number of observations in sample (Column 2): Panel A 1622; Panel B 582; Panel C 277. Columns (1) and (2) show raw means and standard deviations (in parentheses); Column (3) shows the regression adjusted difference including chamber and calendar month fixed effects, standard errors in parentheses, clustered at the (chamber x hearing of entry) level. Significance levels are denoted as follows: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

64
health and public services by the aging OECD populations, but overall, faster growth in low and middle income countries—and particularly China and India—is likely to translate into more pronounced shift of production towards service activities (Figure 6).<sup>10</sup>

# **Figure 6 Shift into services is more pronounced in China and India**

<!-- Start of picture text --> Share in total output, percent<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2030 2005 2030 2005 2030 2005 2030<br>China India All Developing High income<br>Agriculture Processed foods Fossil fuels Manufacturing Services<br><!-- End of picture text --> In order to accommodate this growing share of services in total output, the contribution of other sectors to aggregate production will decrease. For developing countries, the expansion is likely to come at the cost of agricultural output: China’s agricultural output share is likely to decrease by more than one-half, while India’s agricultural production share could decline by one-third. This is driven by sustained large increases in manufacturing productivity in both countries, which underpin their leading growth performance. The fast pace of productivity growth in manufacturing sectors allows their share of total output to remain roughly constant between 2005 and 2030, despite the demand-driven pulling of resources into the service sectors. For the high income countries, the converse is likely to be true. Because productivity in manufacturing among OECD countries grows slower, the share of manufacturing in total output declines from 26 percent in 2005 to 19 percent in 2030.

The changing sectoral structure of Chinese and Indian economies is likely to have profound effects on factor returns. Because services tend to be more skill intensive than other sectors, increasing demand for services is likely to exert upward pressure on skilled wages. In 2005, 79 and 91 percent of total skilled wage bill in China and India is paid to service sector workers, and these shares could rise further by 2030. Demand for skilled workers over the coming decades is likely to be particularly acute in China, where slower population growth will add to the relative scarcity of the white-collar employees. Improvements in education service provision, combined with the fact that younger cohorts tend to be better educated than their older colleagues, are likely to lessen some of the pressures in the labor market. Nonetheless, our baseline scenario envisions an

## **10 Also see**

Table 4 in the Annex for additional details.

12
(NSS). The NSS is conducted annually as a repeated cross-sectional survey of approximately 75,000 rural and 45,000 urban households. We use data from four NSS survey rounds, two prior to the bailout and two after the bailout,<sup>10</sup> and calculate the variable _Consumption_ , which measures monthly per capita consumption expenditure (Mpce). This measure of household consumption is collected consistently across survey rounds and widely used in practice, for example in the calculation of poverty lines.<sup>11</sup>

# **3.4 Additional Variables**

Our dataset contains a number of additional variables, which we use as controls and to construct the instrumental variable described below. Specifically, we measure weather shocks using local variation in monsoon rainfall. Rainfall data are taken from the Indian Meteorological Department and measure total monthly precipitation at the district level. Based on these data, we construct the variable _Rain monsoon_ , which measures total monsoon rainfall between July and September for each year as a fraction of the district’s long-run rainfall average over the same period.<sup>12</sup> Based on this dataset, we also construct a dummy variable indicating years in which a district was exposed to a drought shock. This variable takes on a value of one for any year in which a district’s total monsoon rainfall between the months of July and September was below 75% of the district’s long run precipitation average, measured over the same time period. This definition matches the threshold used by Indian state and local authorities to declare a district as “drought affected”.<sup>13</sup>

> 10 We use data from NSS rounds 63 and 64 for the period prior to the bailout and NSS rounds 66 and 67 for the period after the bailout.

> 11One possible limitation of using NSS data to estimate household consumption is that the NSS survey is representative at the “survey unit,” rather than the district level (a survey unit typically consists of several census districts). We therefore restrict the sample to districts with at least 50 NSS households.

> 12We use 50-year averages for June-September, taken from the _Indian Meteorological Department_ ’s dataset “Long Run Averages of Climatological Normals.”

> 13Data and definitions are available from the _India Meteorological Department_ at http://www.imd.gov.in

13
Argentinean municipalities where water services were privatized, the incidence of child mortality from water-related diseases declined significantly (whereas the incidence of child mortality for other reasons remained stagnant). They therefore provide indirect evidence of improvements in water quality and access.<sup>6</sup> The empirical methodology that we followed is similar to that employed in Galiani, Gertler, and Schargrodosky (2005). Using panel data for the periods surrounding the episodes of privatization and nationalization, we identify differences in sanitation rates between regions that first privatized their water suppliers and later nationalized them and those that did not using a difference-in-difference estimator.<sup>7</sup> Our study has at least two important differences from Galiani, Gertler and Schargrodosky (2005). First, as explained above, we focus on the nationalization of water services, not only on their privatization. Second, our dependent variables are direct measures of the quality of service provided by water companies (access to and quality of water). Health outcomes are important, particularly child mortality, but they tend to be determined by many external factors other than water services. In a middle-income country such as Uruguay, child mortality is rarer than in low-income countries. Therefore, water quality may improve dramatically without any observable change in child mortality.<sup>8</sup> Direct measures of the quality of water services seem more appropriate in such a context.

Our results suggest that although the earlier privatization period had little impact in terms of access to the sanitation network, the nationalization of water services had a positive and statistically significant impact on access, particularly among households in the bottom 25 percent of the income distribution. Nationalization also seems to have led to improved water quality. Indeed, the impact of nationalization on the detection of abnormal levels in microbiological and inorganic water tests is consistently negative and has a relatively large coefficient.

It is important to note that although it may be tempting to conclude from our results that the public sector can provide water services as well as or better than the private sector, this conclusion 5
increased from roughly 20 to nearly 60 percent. Further progress has been achieved in evaluation and debt reporting, domestic borrowing and the governance of guarantees, and debt administration and data security ( **Figure 25** ).

**Figure 25. Percentage of countries meeting minimum DeMPA requirements**

<!-- Start of picture text --> Legal framework<br>80<br>Debt Records Managerial Structure<br>60<br>Segregation of Duties, Staff<br>Debt Management Strategy<br>Capacity and BCP<br>40<br>Debt Administration and Data 20 Evaluation and Reporting<br>Security<br>0<br>Cash Flow Forecasting and Audit<br>Cash Balance Management<br>Loan Guarantees, On lending Coordination with Fiscal<br>Derivatives Policy<br>Coordination with Monetary<br>External Borrowing<br>Policy<br>Domestic Borrowing<br>1st DeMPA Most recent DeMPA<br><!-- End of picture text --> _Notes. The Debt Management Performance Assessment (DeMPA) is a tool used for assessing public debt management performance through a comprehensive set of indicators. Source: World Bank; based on DeMPAs in 22 countries._

# _Strengthening debt management practices_

In order to improve the risk and maturity profile of government debt portfolios, public debt managers have implemented policies to foster the development of local currency bond markets. This allows governments to issue debt in their own currency, thus reducing their dependence on external funding and exposure to exchange rate risk. Second, governments have raised the average life of their portfolio by issuing long-term, fixed-rate instruments —thus, reducing the exposure to interest rate fluctuations and reducing risks of refinancing. More recently, frontier markets in the region have been able to issue 30-year sovereign bonds (Nigeria, Angola and Ghana). Third, sound macroeconomic policies and financial reforms may allow governments to diversify their funding sources. Achieving investment grade —as a result of these policies— may enlarge the pool of creditors, including foreign investors and domestic institutional investors (say, pension funds and insurance companies). Finally, market instruments can also help governments secure the funds needed to deal with the aftermath of large negative shocks —e.g. issuance of explicit state30
from Bangladesh (structural break) and Mexico (randomized experiment) shows similar results: customers are sensitive to interest rates such that in the short-run a 10 percent increase in prices brings roughly a 10 percent drop in credit demand (Dehejia et al 2009, Karlan and Zinman 2016). After three years, Karlan-Zinman find an elasticity of -2.9. True, some customers are willing to pay high prices (in Mexico, roughly 100 percent annually and in Bangladesh roughly 24 percent annually)—but the evidence shows that many are not. A lynchpin of the “win-win” commercial/social vision (i.e., that borrowers are essentially insensitive to interest rates) fails the empirical test.

The main database on microfinance financial performance shows another set of tensions. Table 3 draws on an appendix table in MIX Market (2017), the key industry financial database for institutions focused on the bottom of the market. (However, the database focuses on institutions that tend to be more commercial than those in the broad sweep of the Microcredit Summit numbers described above.) The table disaggregates financial performance by institutions with different legal status. The main lenders by customer number are NGOs, NBFIs (non-bank financial intermediaries), and microfinance banks. Row 1 shows that, together, the MIX counts the three kinds of institutions collectively serving 112 million customers, or 94 percent of the data collected. We will focus on them here (the first three columns of data in Table 3). NBFIs can be for-profit or non-profit and they hold a middle ground between (non-profit) NGOs and (for-profit) microfinance banks. For that reason, contrasting business models are often easiest to see through comparisons of NGOs and microfinance banks, columns 1 and 3.

Row 2 shows that NGOs tend to serve more women than the banks do (87 percent versus 78 percent), and NGOs focus more sharply on micro-enterprise loans (rather than SME loans or household finance; 88 percent versus 51 percent). The comparison is even starker by volume (85 14
generally line up with theoretical predictions: loan delinquency rates increase with interest rates for individual-based lenders. This pattern is also consistent with evidence from the field showing that demand for microcredit is sensitive to price.<sup>2</sup> We do not find this type of pattern for group lenders or village banks, however. The discrepancy is consistent with the claim that group-based contracts serve their intended purpose in these contexts, effectively mitigating information problems by taking the place of collateral as an incentive to repay loans (e.g., Gangopadhyay, Ghatak, and Lensink 2005). Lenders can then raise interest rates on loans without fearing a substantial weakening of portfolio quality.

We find mixed evidence for trade-offs between profitability and outreach. For both individual- and group-based lenders, serving poorer clients is associated with facing higher average costs. The finding follows from the observation that small loans are costlier to serve (per unit lent) relative to larger loans. After controlling for other factors, though, we find that this relatively large cost burden does not preclude profitability: higher costs are met with higher interest rates. Our results on the question of mission drift are promising: financially selfsustainable individual lenders tend to lend to both relatively poorer clients and more women, suggesting that the pursuit of profit and social objectives are not incompatible. However, the typical larger and older institution in our sample does not achieve profitability and deep outreach simultaneously.

# **4. Commercialization**

Commercialized microfinance has been an enduring promise in the field, but not without controversy (e.g., Morduch 2000). Those who argue that commercialization should be _the_ path for microfinance tend to dismiss concerns that commercialization can compromise social achievement—and tend to highlight the way in which commercialization can expand scale. Others argue that compromises between financial and social goals are manifest.

> 2 Dehejia, Montgomery, and Morduch (2009) analyze data from a Bangladeshi lender and find that a ten percentage point increase in the interest rate decreases the demand for credit by between 7.3 and 10.4 percent. Karlan and Zinman (2008) also study customers’ sensitivity to interest rates, with data from South Africa. They find that demand for credit is “kinked”: the customers are more sensitive to interest rate increases than to the lender’s standard rates.

7
A. Sanghi and D. Johnson <!-- Start of picture text --> China India Japan<br>80<br>60<br>40<br>20<br>0<br>South Africa All countries All −−− All United States<br>80<br>60<br>40<br>20<br>0<br>1990 1995 2000 2005 2010 2014 1990 1995 2000 2005 2010 2014 1990 1995 2000 2005 2010 2014<br>Year<br>Percentage of total intermediate goods imports<br><!-- End of picture text --> Figure 11: China’s share of Kenya’s intermediate goods imports is growing Source: UN Comtrade 2015

**Kenyan apparel exports to the US fell initially but are now improving** Under the African Growth and Opportunity Act (AGOA) and the MultiFibre Agreement (MFA), the US received over over 90 percent of Kenya’s clothing exports; exports increased 292 percent from US $78 million to US $306 million (Kaplinsky 2008). After removal of the MFA, Chinese competition decreased Kenyan exports, and threatened to erode gains Kenya made in the US market. Between 2004 and 2006, the value of Kenyan clothing exports to the US dropped 5.1 percent after the first two years of quota removal and dropped 4.6 percent between 2005 and 2011 (Kaplinsky 2008; Onjala 2008). Five factories closed in 2004-2005 with 4,603 job losses —though much fewer than the predicted 25,000 job losses.

After the renewal of AGOA in 2008, Kenyan apparel exports rebounded and are steadily growing (figure 12). Between 2010 and 2014, exports grew at a rate of 13.4 percent per year, faster than the Sub-Saharan Africa growth rate of 5.3 percent. By 2014, Kenya was 37.1 percent of Sub-Saharan Africa’s apparel exports to the US, up from 29.4 percent in 2012. But even with the help of AGOA, Kenya’s exports are still much smaller than China’s. In 2014, Chinese exports were 81 times as much as Kenya’s. Chinese exports grew at a slower rate of 1.2 percent from US $28.8 billion in 2010 to US $30.5 billion in 2014, but they are still 36.7 percent of total US apparel imports. China is becoming a supplier of choice for most US importers because it can meet the large volumes required by large US retailers and apparel companies. China also has lower labor costs, better technology, and better connections in global value chains. If China can manage its market share even with AGOA, if AGOA is removed, Kenyan exports may experience a major loss of their US market share.

12
_Table 3: Regions’ distribution by income level and country._

|||||||I|ncome Le|vel||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Countr||High-Income|||Transition|||Less Develop|ed||Low-Incom|e|Total|
|y||%||||%|||%|||%||
||No|||No|||No|||No|||No|
||.|Category|Country|.|Category|Country|.|Category|Country|.|Category|Country|.|
|Austria|31|4.13|88.57|3|1.55|8.57|1|0.61|2.86|0|0.00|0.00|35|
|Belgium|31|4.13|70.45|7|3.61|15.91|6|3.64|13.64|0|0.00|0.00|44|
|Bulgaria|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|28|13.21|100.00|28|
|<br>Cyprus|0|0.00|0.00|1|0.52|100.00|0|0.00|0.00|0|0.00|0.00|1|
|Czech Republic|1|0.13|7.14|0|0.00|0.00|3|1.82|21.43|10|4.72|71.43|14|
|Germany|297|39.60|74.06|80|41.24|19.95|24|14.55|5.99|0|0.00|0.00|401|
|Denmark|11|1.47|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|11|
|Estonia|0|0.00|0.00|0|0.00|0.00|1|0.61|20.00|4|1.89|80.00|5|
|Greece|5|0.67|9.62|4|2.06|7.69|32|19.39|61.54|11|5.19|21.15|52|
|Spain|17|2.27|29.82|17|8.76|29.82|23|13.94|40.35|0|0.00|0.00|57|
|Finland|19|2.53|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|19|
|France|57|7.60|59.38|37|19.07|38.54|2|1.21|2.08|0|0.00|0.00|96|
|Croatia|0|0.00|0.00|0|0.00|0.00|2|1.21|9.52|19|8.96|90.48|21|
|Hungary|0|0.00|0.00|1|0.52|5.00|0|0.00|0.00|19|8.96|95.00|20|
|Ireland|8|1.07|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|8|
|Italy|69|9.20|62.73|10|5.15|9.09|31|18.79|28.18|0|0.00|0.00|110|
|Lithuania|0|0.00|0.00|0|0.00|0.00|1|0.61|10.00|9|4.25|90.00|10|
|Luxembourg|1|0.13|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|1|
|Latvia|0|0.00|0.00|0|0.00|0.00|1|0.61|16.67|5|2.36|83.33|6|
|Malta|0|0.00|0.00|0|0.00|0.00|1|0.61|50.00|1|0.47|50.00|2|
|Netherlands|38|5.07|95.00|1|0.52|2.50|1|0.61|2.50|0|0.00|0.00|40|
|Poland|1|0.13|1.89|0|0.00|0.00|4|2.42|7.55|48|22.64|90.57|53|
|Portugal|1|0.13|4.00|1|0.52|4.00|15|9.09|60.00|8|3.77|32.00|25|
|<br>Romania|0|0.00|0.00|0|0.00|0.00|1|0.61|2.38|41|19.34|97.62|42|
|Sweden|21|2.80|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|21|
|Slovenia|1|0.13|8.33|0|0.00|0.00|8|4.85|66.67|3|1.42|25.00|12|
|Slovak Republic|1|0.13|12.50|0|0.00|0.00|1|0.61|12.50|6|2.83|75.00|8|
|United Kingdom|140|18.67|78.21|32|16.49|17.88|7|4.24|3.91|0|0.00|0.00|179|
|Total|750|100.00|56.78|194|100.00|14.69|165|100.00|12.49|212|100.00|16.05|1,321|

Notes: Authors’ elaboration on Eurostat data. By-category percentages are defined on column totals. By-country percentages are defined on row totals. High-income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 90% of the sample average. Transition regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 75% of the sample average, but lower than the 90% of the sample average. Less developed regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than the 50% of the sample average, but lower than the 75% of the sample average. Low-income regions are those with an average yearly GDP per capita over the period 2003-2017 lower than the 50% of the sample average.

8
**Table A.4.3: Exogenous variables**

# **_Growth factors_**

|_l_<br>|`gl`|Economy-wide labor productivity growth|
|---|---|---|
|_k_<br>_kt_<br>_i_,<br>|`lambdak(i,kt)`|Capital productivity factor|
|_t_<br>_lt_<br>_i_,<br>|`lambdat(i,lt)`|Land productivity factor|
|_nr_<br>_i_<sup></sup>|`lambdar(i)`|Sector-specific factor productivity|
|_s_<br>_K_|`ksup`|Aggregate (normalized) capital stock|
|_LAND_|`land`|Aggregate land supply|
|**_Trade prices_**|||
|_r_<br>_k_<br>_WPM_ ,|`wpm(k,r)`|World price of imports (CIF)|
|_r_<br>_k_<br>_WPE_ ,|`wpendx(k,r)`|Export price index of competitors|
|_ER_|`er`|Exchange rate and model numéraire|
|**_Fiscal variabl_**<br>_Govnt_<br>_XF_|**_es_**<br>`xf("govnt")`|Volume of government expenditures on goods and services|
|_p_<br>_i_<sup></sup>|`tp(i)`|Production tax|
|_cd_<br>_a_<br>_k_,<br>|`tcd(k,a)`|Sales tax on domestic goods|
|_cm_<br>_j_<br>_k_,<br>|`tcm(k,a)`|Sales tax on import goods|
|_c_<br>_h_<br>_k_,<br>|`Hldts(k,h)`|Subsidies on household consumption|
|_h_<br>_h_<br>|`kappah(h)`|Initial marginal direct tax rates|
|_H_<br>_h_<br>_g_<br>_TR_ ,|`gtrh(h)`|Transfers from government to households|
|_c_<br>_e_<br>|`kappac(e)`|Corporate tax rates|
|_md_<br>|`imptxadj(md)`|Uniform tariff adjustment factor|
|_m_<br>_md_<br>_r_<br>_k_<br>_tr_<br>,<br>,<br>,<br>|`tm(tr,k,r,md)`|Sectoral tariffs by region of origin and tariff regime|
|_e_<br>_r_<br>_k_,<br>|`te(k,r)`|Sectoral export taxes by region of destination|
|_xfl_<br>_l_<br>_i_,<br>|`txfl(i,l)`|Wage tax by sector and labor type|
|_xfk_<br>_kt_<br>_i_,<br>|`txfk(i,kt)`|Capital tax by sector and capital type|
|_sfl_<br>_l_<br>_i_,<br>|`tsfl(i,l)`|Wage subsidy by sector and labor type|
|_sfk_<br>_kt_<br>_i_<sup>_s_ </sup>,<br>|`tsfk(i,kt)`|Capital subsidy by sector and capital type|

41