rafmacalaba/datause-encoder-data huggingface dataset:
standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across countries.<sup>1</sup> Our India sample is comprised of repeated cross-section census data for the formal manufacturing sector at roughly five-year intervals - 1983/84; 1989/90; 1994/95; 2000/01; and 2004/05<sup>2</sup> and census data on the informal sector for 1994/1995. India offers an ideal laboratory for testing the role of institutions on firm life cycle given the large persistent differences in institutions, business environment, and income across different regions in India (Ahluwalia, 2002). Being the seventh largest and second most populous country in the world, the size and income of each of its different states and territories can be likened to other developing countries. For instance, in an interesting comparison, the _Economist_ magazine matches India’s most populous state, Uttar Pradesh, to Brazil in terms of population, Qatar in terms of size of the economy, and Kenya in terms of its GDP/capita. Maharashtra, on the other hand is one of India’s richest states and is equivalent to Mexico in terms of population, Singapore in terms of GDP, and Sri Lanka in terms of GDP/capita.<sup>3</sup> Importantly, there is substantial and well researched heterogeneity in financial and labor institutions across these states. At the same time, comprehensive and centrally collected Census data at the firm-level are available to researchers, thereby sidestepping many of the concerns arising from data comparability in cross-country studies. India is also an interesting case study because some recent studies (e.g. Hsieh and

> 1 The mean response rate across our sample of countries is 70% which is superior to most other survey based studies. For instance, Campello, Giambona, Graham, and Harvey (2011) report response rates of 3 to 7% in their survey of how companies use credit lines during a financial crisis. Other studies in corporate finance report response rates between 7%-9% including Graham and Harvey (2001), Brav, Graham, Harvey and Michaelu (2005), and Lins, Servaes, and Tufano (2010).

> 2 Repeated cross-sections helps us look at life-cycle patterns over a longer period of time in addition to providing us more observations at each point in the life-cycle. It also allows us to examine if our results are consistent over different periods of institutional change in India such as financial liberalization and industry de-licensing.

> 3 All the comparisons are based on 2009 values. See “Comparing Indian states and territories with countries,” _The Economist Magazine, June 21, 2011._

3
nutrition and Cutler, Deaton, and Lleras-Muney 2006; Deaton 2006b; Livi-Bacci 1991; Szreter 1988 who stress the role of public health interventions and in the latter case, institutions). In 1900, water borne diseases accounted for one quarter of reported infectious disease deaths in major (U.S.) cities (Cutler and Miller 2004, p.2). Largely because of investment in clean water and sanitation infrastructure, U.S. “mortality rates fell by 40 percent from 1900 to 1940. Nearly all the mortality decline is accounted for by reductions in infectious diseases” (Cutler and Miller 2004, p.1).<sup>45</sup> These technologies which had developed in the latter part of the nineteenth century only began to be adopted by urban centers when it became possible to raise the capital (from capital markets or local sources) to cover the fixed costs of bringing water from distant places, purifying and distributing it. In addition, urban centers also needed to mobilize the local finances on a continuous basis to maintain the system. The same applied to the sewerage and drainage infrastructure (Cutler and Miller 2005).

Currently, one quarter of all deaths worldwide are the result of infectious diseases mainly concentrated in developing countries—13 million in total ("Infectious Disease" 2006a; Meeting the Urban Challenge 2002). Diarrheal diseases from water borne pathogens affect close to 2.5 billion people across the world. They are responsible for the death of 3-4 million people, a large fraction of whom are children and infants.<sup>46</sup> Although 82 percent of the world’s population supposedly has access to “improved water supply,” this is belied by the statistics on mortality from infectious diseases. The challenge of providing clean water and removing waste remains vast.<sup>47</sup> At present, urban uptake is 30 percent of the global usage of freshwater with households accounting for a third and industry for the balance ("Withdrawing Liquid Assets" 2005). As more people transfer to urban centers, the share of urban consumption and especially household usage will rise dramatically. Supplying clean water and sewerage facilities will

> 45 In England, 60 percent of deaths were because of infectious disease in 1848. The share of mortality arising from infectious disease fell by 95 percent from 1848 to 1971 and this contributed greatly to the increase in life expectancy (Cutler, Deaton, and Lleras-Muney 2006).

> 46 Significant numbers of people are affected by water borne diseases.  Cholera, for instance remains a seasonal problem in many countries and _Vibrio cholerea_ which has passed through the human gut, is more infectious under-scoring the need for hand washing-and the availability of water for doing so. (Codeco and Choelho 2006).

> 47 The coverage of sanitation services lags behind that of water (UNDP 2006).  There are 1.1 billion people around the world without adequate access to clean and safe water and 2.6 billion live without basic sanitation ("Clean Water" 2006).

27
# **Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms**

Reyes Aterido*, Mary Hallward-Driemeier*, Carmen Pagés<sup>**</sup>

> * World Bank<sup>**</sup> Inter-American Development Bank.  The views expressed here do not necessarily represent the views of the World Bank, the Inter-American Development Bank or their Boards of Executive Directors. The authors would like to thank Francis Teal and participants at IADB seminars and at the LACEA conference in Bogota and anonymous referees for valuable comments and suggestions. Corresponding author:  Mary Hallward-Driemeier, World Bank MC3-301, 1818 H St. NW, Washington DC 20433.  Tel:  202-473-9120; Fax:  202-522-1152;  email: mhallward@worldbank.org.
# Migration, Sorting and Regional Inequality: Evidence from Bangladesh

Forhad Shilpi<sup>*†</sup> World Bank JEL Classification : O18; O53; C15 Key Words : Unobserved Heterogeneity, Sorting, Migration, Quantile Decomposition

> * Correspondence contact: F. Shilpi, MSN MC3-300, World Bank, 1818H Street NW, Washington DC 20433, Phone: (202) 458- 7476, Fax: (202) 522 -1151, email: fshilpi@worldbank.org.

> † The views expressed here are those of authors and should not be attributed to World Bank or its affiliates.
characteristics of similar projects. Appendix D presents the details of the imputation process for all variables.

Figure 7 shows the distribution of predicted probabilities of early termination of active projects over the remainder of their contract periods after 2020. The predictions assume that no banking or debt crisis occurs and that the local currency does not depreciate by more than its historical trend against the US dollar. Institutional variables are assumed to remain at their 2018 levels. The median predicted probability of early termination is 0.020 and the mean is 0.029 **.** For 25 projects out of 1,212, the probability is predicted to be greater than 0.15.

# **Figure 7 Distribution of predicted probabilities of early termination during remainder of contract period**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35<br>Probability of early termination<br>Percent of projects<br><!-- End of picture text --> _Source_ : Authors’ calculations.

# **_5.3 Exposure at Early Termination_**

Governments are generally exposed to obligations from the debt and equity financing of an infrastructure project when the PPP is terminated; as the ultimate guarantor of the public infrastructure service, the government steps in to resolve the matter. As indicated in the World Bank’s Guidance on PPP Contractual Provisions, the market practice in the event of a PPP contract termination is that both the lenders and the equity owners be compensated if distress occurred without any fault of either party (World Bank 2019). Without such explicit or implicit guarantees, private finance cannot be effectively mobilized, especially in emerging market and developing economies.

In valuing the fiscal risks from early termination of PPPs, the exposure at distress of each project is calculated using the debt and equity data in the PPI database. The PPI database provides the 20
<!-- Start of picture text --> 160<br>i %<br>i<br>Ge 4a<br>2<br>am 460 oth en 10on<br>GOP per capita percentie<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>a<br>"<br>rp f<br>E j 4 |<br>; Bi f<br>‘i ry Eo<br>Hee acrn ton exh yon,<br>el aie oe ania<br>—— sg —— foe<br><!-- End of picture text -->
Policy Research Working Paper 7485

# **Abstract**

This study presents evidence from a randomized control trial (RCT) in Mongolia on the impact of in-service teacher training and books, both as separate educational inputs and as a package. The study tests for the complementarity of inputs and non-linearity of returns from investment in education as measured by students’ test scores in five subjects. It takes advantage of a national-scale RCT conducted under the Rural Education and Development project. The results suggest that the provision of books, in addition to teacher training, raises student achievement substantially. However, teacher training and books weakly improve test scores when provided individually. Students whose teachers have received training and whose classrooms have acquired books improved their cumulative score (totaled across five tests) by 34.9 percent of a standard deviation, relative to a control group. Students treated only with books improved their total score by 20.6 percent of a standard deviation relative to a control group of students. On the other hand, extra teacher training did not have a statistically significant effect on the total test score. In addition, providing both inputs jointly improved test scores in most subjects, which was not the case when either input was provided individually. This study sheds light on the relevance of supplementing teacher training schemes with appropriate teaching materials in resource-poor settings. The policy implication is that isolated education investments, in settings where complementary inputs are missing, could deliver minimal or no return.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hfuje@ worldbank.org and ptandon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
This scenario suggests a deceleration in the period 2017-2022, with a lower (annualized) proportionate rate of poverty reduction than seen over the period 2008-17. This stickiness is coming mainly from Africa’s weaker progress against poverty, coming from the lower growth rates in this region—a full 1 percentage point lower than for the developing world as a whole.

Arguably a better characterization of the ambitious target is to assume instead that Africa enjoys the same rate of growth as the developing world as a whole. Consider a growth process in which all income levels grow at the same rate—maintaining overall inequality (between and within countries). To quantify such an ambitious target one can use the baseline distribution and project this forward with a higher mean until any given poverty rate is met, and then see what growth rate is required. This automatically takes account of the nonlinearity in how the cumulative distribution function of consumption varies with the poverty line relative to the mean.

The results of this exercise are found in Table 3, Column (1), which gives the growth rate in household consumption per capita needed to bring the $1.25 a day poverty rate down to various levels assuming no increase (or decrease) in inequality within the developing world. The calculations in column (1) are anchored to the 2008 distribution—the last year for which the whole distribution can reliably be constructed from survey data.<sup>27</sup> (The discussion will return shortly to column (2).) A 5% growth rate in mean household consumption per capita without any increase in inequality would get us close to a 4% poverty rate in 2022. However, the required growth rates more than double to get the target down from 4% to 1%. Without lower inequality in 2008, the developing world as a whole would need to achieve China’s growth rate over the last 20 years or so (but without the rise in inequality that China has seen) to get the poverty rate much below 2% within 10 years.

This suggests that a 2% poverty rate by 2022 would be very difficult and could reasonably be seen as close to impossible. A poverty rate of 4.5%—half what we expect to be the poverty rate under the current trajectory—would require a growth rate in mean household consumption per capita of 4.8%, which is almost exactly the expected rate of growth in GDP per capita of 4.9%. But (again) this would require no fall in the household sector share (based on the surveys used to measure poverty) and no increase in inequality within the developing world as a

> 27 For each target poverty rate, I solved backwards to find the growth rate needed to reach that target, holding constant the 2008 Lorenz curve. The accuracy of poverty estimates at low levels is questionable for those countries that use income (rather than consumption) as the welfare indicator. The data base in _PovcalNet_ prefers consumption when it is available, which is for about two-thirds of the surveys. The only region that still uses income surveys extensively is Latin America.

11
13 investment is identical to private domestic savings (From the national accounts basic identity). In this case, though, the exogeneity or constancy of t is even more questionable given the cyclical behavior of fiscal revenues. One way out i5, for example, to make t endogenous on the level of output. Output in turn, could depend, via investment, on the price of equity q. If a DES raises the price of equity, thus, fostering investment and output, by alleviating the fiscal constraint (raising t) it would have an additional beneficial effect on the market price of sovereign debt.

Note that if t represents the fiscal transfer, i.e., implicitly assuming that foreign exchange is no longer the binding constraint on debt servicing, it is perfectly justifiable in the case of DES not to substract profit remittances from t since, clearly, profit remittances are not a government liability. On the other hand, in the case of CDC subtracting the interest on the new debt is legitimate since the new debt also has to be serviced out of domestic fiscal resources.

Having formulated the model we now proceed to examine its comparative static properties.

# 3. SHORT-TERM IMPACT OF VOLUNTARY DEBT REDUCTION.

We assess the short-term comparative static effects o' DES and CDC, implemented at beginning of period prices, on inflation and on equity and sovereign debt prices.
Figure 11.5 Korea-Policy responses and incentives to borrow <!-- Start of picture text --> Monetary policy: components of reserve money Fiscal policy: fiscal impulse and demand<br>growth pressures<br>korea<br>percent Korea percent<br>30- _<br>20- A -<br>-10- / 0 -X l<br>-20- -2-<br>-30 -3<br>1ong 1990 1991 1992 1993 1994 1995 1996 1997 m9e9 1990 1991 1992 1993 1994 1995 1996 1997<br>_ ontribution ot domestic credit tn reserve money growth -r Excess demand<br>r contribution of net foreign assets to reserve money growth -F,scaI impulse<br>reserve money growth (percent of GDP)<br><!-- End of picture text --> Differentials between domestic deposit rates and Variability of unpredictable component of US LIBOR nominal exchange rate movements.

<!-- Start of picture text --> percent Korea<br>percent 10 -<br>12- Korea s-<br>10-~~~~~~~~~~~~~~0<br>10 3 vnbdty of -.<br>3 ~ ~ urp,ed,.t.bl corpor.r~~~~~4<br>2-<br>e- 0 0 - O_~~~~~~~~~~~~~~0<br>-2 4- B3 B- I-<br>1.98 1990 1992 1994 1996 1990 1099<br>-domestic deposit rates<br>difterential between domestic deposit rates and US LIBOR<br>(adjusted for ex post exchange rate movements)<br><!-- End of picture text --> The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate on a constant and a time trend.

27
(2013), which shows that the BTA increases the wage of unskilled workers – who are usually poor people. This is an important result that merits a much deeper investigation.

# **7.3 Rural versus urban households**

**Table 7.3 Job creation in rural versus urban areas**

||Δ share of<br>manufacturing<br>employment|Δ share of<br>service<br>employment|Δ share of<br>manufacturing<br>hours|Δ share of<br>service hours|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Change in tariffs*rural-1|-0.733<sup>**</sup>|-0.239|-0.755<sup>**</sup>|0.029|
||(0.316)|(0.293)|(0.345)|(0.361)|
|Change in provincial tariffs|-0.808<sup>***</sup>|-0.268<sup>*</sup>|-0.988<sup>***</sup>|-0.624<sup>**</sup>|
||(0.127)|(0.160)|(0.108)|(0.252)|
|Rural-1(1 for rural areas)|-0.071<sup>**</sup>|-0.103<sup>***</sup>|-0.083<sup>***</sup>|-0.101<sup>***</sup>|
||(0.027)|(0.030)|(0.030)|(0.036)|
|Share of manufacturing employment-1|-0.419<sup>***</sup><br>(0.017)||||
|Share of service employment-1|0.013<br>(0.104)||0.017<br>(0.088)||
|Share of manufacturing hours-1||-0.387<sup>***</sup><br>(0.009)|||
|Share of service hours-1||-0.000||-0.004|
|||(0.035)||(0.038)|
|Change in share of provincial manufacturing<br>workers between 2002 and 1998|||-0.432<sup>***</sup><br>(0.018)||
|Change in share of provincial service workers<br>between 2002 and 1998||||-0.383<sup>***</sup><br>(0.011)|
|Constant|-0.042<sup>***</sup>|0.077<sup>***</sup>|-0.056<sup>***</sup>|0.061<sup>**</sup>|
||(0.013)|(0.020)|(0.012)|(0.026)|
|_N_|18957|18957|18957|18957|
|adj._R_<sup>2</sup>|0.190|0.190|0.205|0.179|

Notes: - Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

- Control variables are Household head (HH)’s ethnicity, HH age, HH gender, HH vocational education dummy, HH high school dummy, HH middle school dummy, HH primary school dummy, spouse vocational education dummy, spouse high school dummy, spouse middle school dummy, spouse primary school dummy, household size, and number of working age members. All of these household characteristics are reported in 2002. Standard errors are clustered at province level.

Impact of the BTA on employment may be different between rural and urban areas. We pay attention to the interaction between provincial tariff declines (��) and a dummy variable for rural areas. Table 7.3 shows that the interaction terms (Change in tariffs*rural-1) are negative and statistically significant at the 5 percent level for both measures of manufacturing employment 34
# **Table 12: Firm-level Innovation and the Business Environment in Sri Lanka Effects across management training types**

|**(1)**<br>**(2)**<br>**(3)**|**(4)**<br>**(5)**|
|---|---|
|**In the last three years, has**|**this establishment:**|

|**Dependent variable--In thepast 3years, has the firm:**|**introduced**<br>**new**<br>**products or**<br>**services?**|**significantly**<br>**improved an**<br>**existing**<br>**product?**|**introduced**<br>**new or**<br>**improved**<br>**business**<br>**processes?**|**changed the**<br>**way work is**<br>**organized ?**|<br> <br>**introduced a**<br>**new method**<br>**of pricing**<br>**goods or**<br>**services?**|
|---|---|---|---|---|---|
|How many visits from officials from the Inland revenue department in past year|0.003|-0.157|-0.380|-0.697+++|-0.054|
||(0.249)|(0.186)|(0.265)|(0.236)|(0.173)|
|What is the highest level of education that you have completed?|0.117+++|0.086+++|0.104+++|0.028|-0.015|
||(0.033)|(0.026)|(0.033)|(0.027)|(0.025)|
|Training: Secondary or technical school|-0.842|-0.366||0.076|-0.160|
||(0.777)|(0.495)||(0.474)|(0.466)|
|Training: College or university|-0.811|0.163||-0.037|-0.139|
||(0.758)|(0.540)||(0.573)|(0.625)|
|Training: Short course offered government / NGO|-0.457|-0.300|0.602|-0.102|0.121|
||(0.503)|(0.403)|(0.452)|(0.407)|(0.382)|
|Training: Short course offered by consulting firm|0.328|0.426|0.937++|0.053|0.605+|
||(0.436)|(0.351)|(0.392)|(0.403)|(0.345)|
|Training: Paid for individual consultancy|0.336|0.464|0.812++|0.529+|0.548+|
||(0.366)|(0.320)|(0.363)|(0.317)|(0.315)|
|Does your firm use the internet?|0.614+++|0.221|0.068|0.486++|0.376++|
||(0.237)|(0.193)|(0.254)|(0.205)|(0.188)|
|Has the firm used a consulting service in the last 3 years?|1.094+++|0.836+++|1.182+++|0.766+++|0.212|
||(0.286)|(0.250)|(0.298)|(0.262)|(0.256)|
|What percentage of your sales are made to the following: {Foreign firms}|0.018++|0.003|0.009|0.002|0.002|
||(0.008)|(0.008)|(0.010)|(0.008)|(0.009)|
|What percentage of the start-up capital came from : {Local Banks}|0.003|0.002|-0.004|-0.002|0.004|
||(0.004)|(0.003)|(0.004)|(0.003)|(0.003)|
|What percentage of your sales are made on credit?|0.005|0.006+|0.005|0.010+++|0.009+++|
||(0.004)|(0.003)|(0.004)|(0.003)|(0.003)|
|Constant|-6.824|-6.944|-8.195|-5.063|0.627|
||(260.990)|(235.779)|(172.337)|(139.466)|(1.044)|
|Observations|529|528|479|543|585|
|Pseudo R-squared|0.315|0.169|0.270|0.181|0.183|
|Province fixed effects|Y|Y|Y|Y|Y|
|Sector fixed effects|Y|Y|Y|Y|Y|

Notes: Standard errors in parentheses. + p<.1 ++ p<.05 +++ p<.01. Source: SLLSE (2008 Booster Sample) 43