rafmacalaba/datause-encoder-data huggingface dataset:
even more affected by outliers. We see very large standard errors, and imprecise impacts on levels of profits and sales.

Appendix Table 6.5 shows impacts on different components of total employment. The only outcome that is statistically significant is insourcing firms using fewer apprentices or interns, but we can also not reject that treatment effects are jointly zero for this outcome too.

|**Appendix Table 6.1: Impact on Firm**|**Growth Conditi**|**onal on Su**|**rvival**|||||
|---|---|---|---|---|---|---|---|
||I.H.S.|I.H.S.|I.H.S.|I.H.S.|Sales &|Total|I.H.S.|
||Monthly<br>Sales|Yearly<br>Sales|Monthly<br>Profits|Yearly<br>Profits|Profits<br>Index|Employment|Employment|
|**Panel A: Impacts in First Follow-up**|**Survey (One-yea**|**r after int**|**ervention s**|**tarted)**||||
|Assigned to Insourcing|0.265|0.303|0.129|0.407|0.053|-0.300|0.028|
||(0.367)|(0.420)|(0.555)|(0.535)|(0.073)|(1.024)|(0.095)|
|Assigned to Outsourcing|0.034|0.589|0.206|0.307|0.055|-0.632|0.028|
||(0.366)|(0.371)|(0.534)|(0.569)|(0.065)|(1.040)|(0.094)|
|Assigned to Training|-0.578|-0.226|-0.274|0.229|-0.052|-0.359|0.024|
||(0.446)|(0.475)|(0.575)|(0.549)|(0.077)|(1.015)|(0.100)|
|Assigned to Consulting|-0.280|-0.226|0.089|-0.739|-0.067|-0.160|0.020|
||(0.397)|(0.454)|(0.546)|(0.608)|(0.077)|(1.022)|(0.096)|
|Mean of Control Group|13.384|15.422|11.401|13.477|0.168|9.825|2.604|
|Sample Size|659|659|659|659|659|664|664|
|P-value: all treatments zero|0.342|0.135|0.909|0.316|0.297|0.980|0.998|
|P-value: all treatments equal|0.214|0.094|0.804|0.199|0.182|0.969|1.000|
|**Panel B: Impacts in Second Follow-**|**up Survey (Two-**|**years after**|**interventi**|**on started)**||||
|Assigned to Insourcing|0.694|0.505|0.498|0.369|0.098|0.010|0.073|
||(0.464)|(0.407)|(0.522)|(0.562)|(0.083)|(0.731)|(0.106)|
|Assigned to Outsourcing|0.795*|0.747**|0.373|1.024**|0.147*|0.219|0.115|
||(0.467)|(0.344)|(0.549)|(0.442)|(0.076)|(0.764)|(0.106)|
|Assigned to Training|0.151|0.310|-0.242|0.381|0.024|0.291|0.046|
||(0.527)|(0.425)|(0.593)|(0.518)|(0.088)|(0.794)|(0.108)|
|Assigned to Consulting|0.591|0.333|0.331|0.751|0.089|0.518|0.136|
||(0.462)|(0.401)|(0.550)|(0.515)|(0.086)|(0.737)|(0.104)|
|Mean of Control Group|12.759|15.513|10.989|13.499|0.120|7.488|2.345|
|Sample Size|627|627|627|627|627|642|642|
|P-value: all treatments zero|0.356|0.120|0.686|0.080|0.232|0.951|0.709|
|P-value: all treatments equal|0.545|0.241|0.604|0.184|0.329|0.916|0.809|

Notes: Regressions control for stratification batch fixed effects and baseline value of outcome. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. I.H.S. denotes the inverse hyperbolic sine transformation. Firms that are closed are coded as having zero profits and sales. **Monthly sales** is real (January 2018) sales in the past month; **Yearly Sales** are real sales in the past year; **Monthly profits** and **Yearly profits** are real profits in the past month and past year respectively. **Sales and Profits Index** is the average of standardized z-scores of these first four sales and profits measures. **Total Employment** is total number of workers.

73
adequate, regular and quality water supply in rural areas compared to the KWA schemes. Jalanidhi schemes also reported fewer breakdowns and water shortages indicating better O&M. The demand responsive community based approach of Jalanidhi was more successful in generating higher consumer satisfaction with service delivery and cost of service in spite of charging higher monthly tariffs compared to the KWA schemes. Overall, the Jalanidhi schemes performed better than the KWA schemes in all important dimensions in which comparisons were done.

When compared to other community managed schemes, Jalanidhi schemes performed better in O&M and overall performance. But the main success of Jalanidhi was in creating stronger institutions, which is one of the prerequisites for the long-term sustainability of the community based approach. The substantial time and effort that was spent in mobilizing communities, creating capacities, and involving communities in planning and implementation of the Jalanidhi schemes possibly explain their superior institutional performance compared to other community managed schemes. However, achieving full O&M cost recovery remains an elusive goal for Jalanidhi schemes in particular and all community managed schemes in general. This coupled with relatively low bank balances for the majority of the BGs severely limits their ability to undertake expensive maintenance work when needed, which in turn might threaten the long-term sustainability of the community based approach to rural water supply.

The findings of this paper suggest that the community based approach can be a superior alternative to traditional supply driven models in expanding and improving water service delivery in rural areas. Success of the community based approach demonstrates that the people are willing to contribute towards the capital costs of the schemes and pay for the water that they use for better service delivery. However, to ensure long-term sustainability of the community based schemes, more attention needs to be paid in creating stronger beneficiary level institutions, including capacity development for financial management for successful operation and management of these schemes. In addition, institutions need to be created to provide operational and financial support to these schemes when needed.

# References

Abrams, L., Palmer, I., and Hart, T. (1998), Sustainability Management Guidelines. Pretoria: Department of Water Affairs and Forestry, South Africa Andres, L. and B. Dasgupta (2016), “Functionality of water schemes in Nigeria” World Bank. (Forthcoming).

- Black, Maggie, 1978-1998 Learning What Works, “A 20 Year Retrospective View on International Water and Sanitation Cooperation.” World Bank, “World Bank Policies on Participation”. (2011). Online at: <u>http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTSOCIALDEVELOPMENT/EX</u> ~ ~ ~

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Barnighausen, T., V. Hosegood, I. M. Timaeus, and M. L. Newell. 2007. 'The socioeconomic determinants of HIV incidence: evidence from a longitudinal, population-based study in rural South Africa', _AIDS_ , 21 Suppl 7: S29-38. Barnighausen, T., F. Tanser, Z. Gqwede, C. Mbizana, K. Herbst, and M. L. Newell. 2008. 'High HIV incidence in a community with high HIV prevalence in rural South Africa: findings from a prospective population-based study', _AIDS_ , 22: 139-44. Bloom, S. S., M. Urassa, R. Isingo, J. Ng'weshemi, and J. T. Boerma. 2002. 'Community effects on the risk of HIV infection in rural Tanzania', _Sex Transm Infect_ , 78: 2616.

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Brown, J. M., A. Wald, A. Hubbard, K. Rungruengthanakit, T. Chipato, S. Rugpao, F. Mmiro, D. D. Celentano, R. S. Salata, C. S. Morrison, B. A. Richardson, and N. S. Padian. 2007. 'Incident and prevalent herpes simplex virus type 2 infection increases risk of HIV acquisition among women in Uganda and Zimbabwe', _AIDS_ , 21: 1515-23.

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- Burundi, Republique du. 2010. "Mise En Oeuvre De La Declaration

- D'Engagement Sur Le VIH/SIDA " In.

Central Bureau of Statistics (CBS) [Kenya], Ministry of Health (MOH) [Kenya], and ORC Macro. 2003. 'Kenya Demographic and Health Survey 2003'.

- Central Statistical Agency [Ethiopia], and ICF International. 2012. "Ethiopia Demographic and Health Survey 2011 " In.

- Central Statistical Agency [Ethiopia], and ORC Macro. 2006. "Ethiopia Demographic and Health Survey 2005." In.

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- "Swaziland Demographic and Health Survey 2006-07." In.

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71
# **1 Introduction**

Some 844 million people lack clean drinking water and 2.4 billion people do not have improved sanitation, most of which are living in low- and middle-income countries (LMICs).<sup>1</sup> An estimated US$ 1.7 trillion is needed to finance the goal of universal access by 2030 (Hutton and Varughese, 2016). With two-thirds of the world’s population expected to live in cities by 2050, finding scalable and sustainable solutions to expand reliable urban water and sanitation services is critical. However, basic service provision is not keeping up with rapid urbanization. Africa’s cities, for instance, grew by 80% between 2000 and 2015, while access to piped water declined from 40% to 33% (World Bank, 2017).

The primary route for expanding services in urban areas is through public utilities, but utilities are struggling to deliver reliable services to connected households let alone increase coverage (Trimble et al., 2016; World Bank, 2017; Soppe et al., 2018). Revenue collection has become a major stumbling block for utility performance (Ahluwalia, 2002; World Bank, 2017). Losses from nonpayment of service use bills are significant. Utilities worldwide failed to collect an estimated US$ 39 billion of billable water and US$ 96 billion in electricity charges each year (Liemberger and Wyatt, 2019; Northeast Group, 2017). Nonpayment for services lowers effective prices, and when effective prices fall below marginal cost, each new customer becomes a financial liability and may lead to rationing (Burgess et al., 2020; McRae, 2015).<sup>2</sup> Water rationing is not only inconvenient but can have negative impacts on health (Galiani et al., 2005; Ashraf et al., 2021). There is well-documented concern over service quality degradation worldwide from poorly maintained infrastructure, and the vicious cycle between low payment and deteriorating quality of services (Jiménez and Pérez-Foguet, 2011; Cronk and Bartram, 2017; Foster et al., 2020; Dzansi et al., 2018; World Bank, 2017).

More financial stress comes from the problem of paying for the significant last mile connection costs that constrain household access to the utility’s services (WaterAid, 2007; Golumbeanu and Barnes, 2013; Lee et al., 2020). Significant investments in urban trunk infrastructure are underutilized when households close to water and sewer lines do not connect (Kennedy-Walker et al., 2020). While subsidies have been effective at increasing household connections (Lee et al., 2020; Guiteras et al., 2015), they are costly, potentially regressive, and are limited by public budget constraints (Abramovsky et al., 2020).

Providing credit to households to amortize upfront connection costs offers an attractive

> 1World Bank (water): https://www.worldbank.org/en/topic/watersupply; World Bank (sanitation): https://www.worldbank.org/en/topic/sanitation

> 2Many utilities have tariffs that even when fully paid do not cover operating costs, and, in settings where only a minority have access to piped services, introduce a regressive subsidy in favor of connected households (Abramovsky et al., 2020).

1
sectoral movers. Exploring these issues further and extending the model by linking structural change to geographical migration could prove a fruitful exercise, particularly in the Brazilian context.

# **4 Brazil in the Next Decade: How Trade Policy Affects a Business as**

# **Usual Scenario**

A central question of this paper is assessing the poverty effects of trade policy reforms in the long run, when many structural adjustment forces shape the income generation process. Our starting point consists of using our CGE model to build a business as usual scenario depicting the evolution of the Brazilian economy in the next decade. This evolution should not be considered as a statistical forecast, but rather as a consistent “projection” of the economy in a future where inter-sectoral productivity growth differentials, skill upgrading, and migration of labor out of farming activities play major roles. This Business as Usual (BaU) projection is then contrasted with alternative scenarios where trade policy reforms are added. The following subsections, describe in details the macro and micro results for the BaU and trade scenarios.

# **4.1 The Business as Usual Macro Results**

In the BaU scenario, real GDP for Brazil is projected to grow (from 2005 onwards) at the fairly sustained yearly rate of 3.3%; this is somewhat optimistic when compared with the recent two decades’ (1980-2000) rate of 2%. This GDP growth performance is backed up by strong factor productivity growth rates. As explained above, productivity in the agriculture sector is factor neutral and its growth rate is exogenously set at 2.9% per year<sup>29</sup> ; in the non-farm sectors, growth of labor productivity is calibrated at 1.02% per year and growth of capital productivity at 0.82% per year.

These differences in productivity growth rates across sectors, combined with faster growth of the supply of skilled versus unskilled labor generate significant structural adjustments, in line with those observed for the last decade. The changes in the structure of labor markets, shown in Table 2, are of particular relevance for poverty and income distribution trends. On the supply side, education increases the supply of skilled workers which is growing at a 2.0% annual rate versus a yearly 1.6% growth rate for the unskilled labor supply. Additionally, through out-migration, the supply of unskilled workers in

> 29 Note that this value is at the high end of the estimations of TFP growth rates by Martin and Mitra (1999), see page 15.

27
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The liquidity criterion (i) is of particular importance in our case, given that many DRs from emerging economies display infrequent trading. This, in turn, would invalidate the use of the cross-market premium, since _it would reflect spurious price divergences due to the lack of transactions_ . Since price quotes correspond to the latest trade, quotes recorded at different dates may not represent a price divergence but rather the evolution of prices over time. Appendix I discusses more extensively how trading frequency affects the behavior of the cross-market premium.

These restrictive selection criteria seem necessary for a meaningful analysis, but also reduce substantially the number and type of candidate countries and stocks. We work with 76 stocks (out of 133 DRs that trade in the NYSE of the NASDAQ) from nine emerging economies: Argentina (8 stocks), Brazil (24), Chile (12), Indonesia (2), Mexico (13), South Africa (7), Korea (6), Russia (2), and Venezuela (2). Appendix Table 1 reports the companies that are included in the respective portfolios and the period for which the premium is calculated.

The data needed to calculate the premium (the dollar price of the stock in the domestic market, the price of the DR in New York, and the number of underlying shares per unit of the depository receipt) come from Bloomberg. For Argentina, Brazil, Chile, and Venezuela we use the closing price both in the domestic market and in New York. For Asian markets, which are already closed when New York opens, as well as for Russia and South Africa, we use instead the closing price (and the exchange rate) in the domestic market and the opening price in New York, to keep distortions due to time differences to a minimum.

# **4. Methodology**

As noted, we expect higher transaction costs to lead to broader bands of no-arbitrage. To the extent that high transaction costs are associated with a lower level of financial integration, the estimated width of the no-arbitrage bands provides a measure of effective integration.

The existence of transaction costs basically implies that two different regimes exist, an arbitrage and a no-arbitrage regime. If, on the one hand, the difference between the two prices is smaller than the transaction costs, arbitrage will not take place and the 8
4 If the previous World Bank estimates of imm-gration to the major receiving countries should be revised upward (as I argue below), then obviously the estimetes of net emigration for most of the major sending countries will also need to be revised upward. The World Bank estimates for each of the main receiving countries are reviewed below. This is followed by brief reviews of other countties for which changes in the estimates are recommended. Countries for which the current estimates seem quite reasonable and do not require any changes (or for which I do not have sufficient information to recommend changes) are usually not discussed in the text. A summary of the recommendations is given in the appendix.

# MAJOR RECEIVING COUNTRIES

# United States

Immigration to the United States is currently a very significant factor in the total population growth in the country. Between April 1, 1980 and January 1, 1987, for example, 29 percent of the increase in the U.S. population was due to net civilian immigration (U.S. Bureau of the Census, 1987). In recent years, the United States has accepted at least as many foreign-born persons for legal permanent residence as all other countries in the world combined (Teitelbaum, 1987). Moreover, a careful consideration of recent trends and patterns in U.S. immigration, the political climate in the United States, lags in the administrative system for legal immigration, and continuing economic disparities between the United States and source countries does not support an assumption of rapidly declining immigration before the end of the century. I would strongly recommend an assumption of 2,775,000 net immigrants for 1980-1985, increasing to a level of 2,900,000 for 1985-90, and declining very gradually thereafter. The reasons for this conclusion are outlined below.

Legal Immigration. As shown below in Table 1, legal immigration to the United States has been fairly constant at an average level of 570,965 from FY1980 to FY1986 (Immigration and Naturalization Service, 1987). Recently released statistics for FY1987 show a continuation of this trend, with a total immigration level of 601,516 persons in that year (Immigration and Naturalization Service, 1988a). The FY1986 and FY1987 streams are the largest and second largest annual inflows of legal immigrants to the United States since 1924 (see Figure 1).

The last column of Table 1 indicates that only about 62 percent of total immigrants are new arrivals who apply for permanent residence status from outside the United States. The remaining 38 percent are persons already present in the United States, who adjust their status to become immigrants without leaving the country. About 45 percent of those who adjust their status originally came to the United States as refugees and most of the rest arrived on visitors visas. The majority of status adjusters originally arrived in the United States either in the same year that they became permanent residents or in the previous two years. Although it is important to keep the year of actual arrival in mind, the figures for the total number of immigrants (new arrivals plus status adjusters) still give a reasonable
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Why infrastructure matters

In common with the rest of the continent, West Africa’s growth performance improved markedly in the 2000s relative to the 1990s. The overall improvement in per capita growth rates has been estimated at 2 percentage points, of which 1.1 percentage points can be attributed to better structural policies and 0.9 percentage points to improved infrastructure **.** This contribution came mainly from the ICT revolution, while deficient power infrastructure held growth back (figure 1).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

Infrastructure’s contribution to annual per capita economic growth in African regions, 2003–07, in percentage points <!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>North  West  East  Southern  Central  Africa<br>Africa Africa Africa Africa Africa<br>telecom power roads<br><!-- End of picture text -->

Source: Calderon 2009.

Mali’s sustained growth has been driven primarily by telecommunications services, but also by transport, gold mining, and well-performing cereal harvest (World Bank 2007a). Between 2003 and 2006, Mali’s economy grew at an average annual rate of 5.3 percent, despite external shocks, including a decline in cotton prices and rise in oil prices.

Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 40 percent of the productivity handicap faced by Malian firms (figure 2), with the remainder being due to poor governance, red tape, and financing constraints. Power is the infrastructure constraint that weighs most heavily on Malian firms, with transport a close second.

3
Fourth, for the first time, we simultaneously analyzed the effect of supply side shocks, i.e. financial crisis, and demand side ones. Our results confirm the importance of demand shocks, especially for sectors producing durable goods. Interestingly, the effect of demand shocks is independent and additional to that of the financial crisis. In the present context this can explain the magnitude of the drop in trade flows as a consequence of a double shock from both supply and demand side.

Concluding, we have some limited evidence about the impact on exports of alternative policies applied during previous banking crises which did not seem to have a significant positive impact on the exporters. Rather, it emerges that general economic and financial development and access to alternative sources of finance helps to reduce the adverse impact of a financial crisis. The caveat here is that the available dataset on policy interventions is rather limited and specific policies targeted at exporters might be more relevant than general interventions intended to ease the solvency and liquidity problems of the banking sector. Unfortunately, a comprehensive database of export oriented interventions does not yet exist and thus constitutes an attractive avenue for future research.

22