rafmacalaba/datause-encoder-data huggingface dataset:
economies that will be affected by agricultural liberalization negotiated in the current WTO round derive their income from agriculture and might thus experience similar changes as Vietnam.

# **References**

Baland, J. M. and J. Robinson (2000). “Is Child Labor Inefficient,” _Journal of Political Economy_ 108, 663-79.

- Basu, K. and P. H. Van (1998). “The Economics of Child Labor,” _American Economic Review_ , 88, 412-427.

- Benjamin, D. and L. Brandt (2004). "Agriculture and Income Distribution in Rural Vietnam under Economic Reforms: A Tale of Two Regions," ."  in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ .  Washington DC: World Bank.

- Desai, J. (2000).  "Vietnam through the Lens of Gender: Five Years Later," _Food and Agriculture Organization of the United Nations Manuscript_ , November.

Dollar, D., and A. Kraay (2001). "Trade, Growth, and Poverty." World Bank Mimeo.

- Edmonds, E. and N. Pavcnik (2003a).  “The Effect of Trade Liberalization on Child Labor,” Dartmouth College Manuscript.

- Edmonds, E. and N. Pavcnik (2003b).  "The Relationship between Product Market Integration and Child Labor Supply:  Evidence from Vietnam,"  Dartmouth College Manuscript.

Edmonds, E. and N. Pavcnik (2003c). "Trade Liberalization and the Allocation of Labor between Households and Markets in a Poor Country," Dartmouth College Manuscript.

Edmonds, E. and C. Turk (2004). "Child Labor in Transition." in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ . Washington DC: World Bank.

General Statistical Office (1994): _Vietnam Living Standards Survey 1992-93_ . Ha Noi, Vietnam.

General Statistical Office (1999): _Vietnam Living Standards Survey 1997-98_ . Ha Noi, Vietnam.

Glewwe, P. and P. Nguyen (2004). "Economic Mobility in Vietnam in the 1990s." ." in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ . Washington DC: World Bank..

40
# Figure 7. Risks to potential growth prospects

A. Forecast errors in global, advanced economy and EMDE investment growth Percentage points B. Forecast errors in EMDE regional investment growth Percentage points <!-- Start of picture text --> 2 World Advanced economies EMDEs<br>0<br>-2<br>-4<br>-6<br>1 2 3 4 5 6 7 8 9<br>Forecast horizon<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>EAP ECA LAC SAR<br>0<br>-2<br>-4<br>-6<br>1 2 3 4 5 6 7 8 9<br>Forecast horizon<br>D. Deviation from baseline scenario for<br>EMDE potential growth, adjusting for<br>risks<br>Percentage point Labor market reforms risk<br>Policy risk<br>Forecast error risk<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> C. Global potential growth, adjusting for risks D. Deviation from baseline scenario for EMDE potential growth, adjusting for risks <!-- Start of picture text --> Percent Labor market reforms risk<br>3 Policy risk<br>Forecast error<br>Baseline<br>2<br>1<br>0<br>2011-21 2022-30 2022-30<br>E. Potential growth with more frequent<br>natural disasters<br>Percent<br>6<br>Baseline Natural disasters damage<br>4<br>2<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World Advanced EMDEs<br>economies<br><!-- End of picture text --> E. Potential growth with more frequent natural disasters F. Potential growth after a global recession in 2023 <!-- Start of picture text --> Percent<br>6 Baseline Recession damage<br>4<br>2<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World Advanced EMDEs<br>economies<br><!-- End of picture text --> Sources: Consensus Economics, Haver Analytics, World Bank.

Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. B. Data for 34 countries, of which 13 EMDEs (3 in EAP [Indonesia, Malaysia, Thailand], 3 in ECA [Hungary, Poland, Romania], 6 in LAC [Argentina, Brazil, Chile, Colombia, Mexico, Peru], India in SAR) since 2000. GDP-weighted averages (at 2010-19 exchange rates and prices). Forecast error is the difference between actual and forecast investment growth; a negative error indicates overoptimism.

C. D. GDP-weighted arithmetic averages. Baseline scenario assumes that investment growth will match consensus forecasts for one- to nine-year-ahead investment growth for 2022-30. Correction for forecast errors assumes that investment growth will fall by the country-specific average historical forecast error over 1-9-
# Table 3:

# DESCRIPTIVE STATISTICS

|Variable|Number of<br>observations|Mean|Standard<br>deviation|
|---|---|---|---|
|Dependent Variable||||
|Aggregate crop output index|765|1.338|1.168|
|<br>Fertilizer consumption, nutrient tons/10 sq km|738|23.784|30.997<br>|
|Net investment in draft animals, number/lO sq km|228|6.755|17.102|
|<br>Net investment in milk animals, number/lO sq km|228|17.974|27.691|
|Net investment in small stocks, number/10 sq km|228|5.948|15.426|
|Net investment in pumps, number/10 sq km|228|1.645|2.034|
|<br>Credit advanced to rural sector, '000 Rs./10 sq km|765|283.991|421.445<br>|
|Cooperative credit advanced to agriculture|765|93.615|203.583|
|<br>Agricultural real wage, Rs./manday|765|5.294|2.165<br>|
|Agricultural employment, persons/10 sq km|170|235.492|196.889|
|<br>Nonagricultural employment, persons/10 sq km|170|153.989|206.158|
|Indegendent Variable||||
|Aggregate real crop price index|765|0.851|0.328|
|<br>Real price of fertilizer|765|3.459|0.493|
|<br>Annual urban wage, real|765|4373.277|1406.924|
|<br>Canal irrigation, '000 ha/10 sq km|765|0.068|0.101|
|Number of regulated markets/10 sq km|765|0.019|0.025|
|Number of villages with primary schools/10 sq km|765|1.289|0.663|
|<br>Number of villages with electricity/10 sq km|765|0.976|0.865|
|Total road length, km/10 sq km|765|5.369|4.986|
|<br>Number of rural and semi-urban branches||||
|of commercial banks/10 sq km|765|0.101|0.132|
|<br>Number of cooperative bank branches/10 sq km|765|0.031|0.026|
|Number of agricultural cooperative<br>societies/10 sq km|765|0.436|0.277|
|<br>Number of land development banks/10 sq km|765|0.010|0.006|
|<br>Annual rainfall, mm|765|1120.059|964.609|
|<br>Soil moisture capacity index|85|2.349|1.01|
|<br>Length of rainy season, months|85|3.653|1.368|
|<br>Excess rainy months, number|85|1.236|1.394|
|<br>Number of cold months|85|0.935|1.313|
|Percentage of area liable to flooding|85|1.389|3.532|
|<br>Percentage of area potential for irrigation|85|30.001|31.909|

19
Table S.5: Baseline Results

||BASEI|BASEII|BASEIII|
|---|---|---|---|
||(1)|(2)|(3)|
|Log Ruggedness|0.132<sup>_∗∗∗_</sup><br>(0.032)|0.124<sup>_∗∗∗_</sup><br>(0.036)|0.129<sup>_∗∗∗_</sup><br>(0.035)|
|Log of Rainfall|-.040|-.058|-.054|
||(0.071)|(0.071)|(0.073)|
|Log dist to coast|-.029|-.051|-.034|
||(0.048)|(0.051)|(0.048)|
|Population Density|0.093<sup>_∗∗∗_</sup>|0.089<sup>_∗∗∗_</sup>|0.092<sup>_∗∗∗_</sup>|
||(0.016)|(0.017)|(0.017)|
|Log of Surface Area|0.075<sup>_∗∗∗_</sup><br>(0.02)|0.087<sup>_∗∗∗_</sup><br>(0.022)|0.078<sup>_∗∗∗_</sup><br>(0.02)|
|Length>than 50km|-.104<sup>_∗∗_</sup>|-.120<sup>_∗∗∗_</sup>|-.110<sup>_∗∗∗_</sup>|
||(0.042)|(0.041)|(0.042)|
|Estimate|-.021|-.019|-.015|
||(0.059)|(0.058)|(0.058)|
|Contract|-.057|-.060|-.058|
||(0.073)|(0.075)|(0.074)|
|Log of GDP per capita||-.083<sup>_∗∗_</sup><br>(0.038)||
|Log of GDP pc (1985)|||-.030|
||||(0.046)|
|Pilot Country|-.054|-.054|-.062|
||(0.081)|(0.087)|(0.083)|
|Foreign frm or JV|0.284<sup>_∗∗_</sup>|0.263<sup>_∗∗_</sup>|0.283<sup>_∗∗_</sup>|
||(0.124)|(0.127)|(0.125)|
|World Bank|0.0009|0.002|0.009|
||(0.09)|(0.091)|(0.089)|
|Bilateral Donor|0.163|0.192|0.179|
||(0.131)|(0.14)|(0.136)|
|East Asia & Pacifc|-.496<sup>_∗∗∗_</sup>|-.435<sup>_∗∗∗_</sup>|-.481<sup>_∗∗∗_</sup>|
||(0.089)|(0.104)|(0.096)|
|Europe & Central Asia|-.131|-.016|-.045|
||(0.089)|(0.104)|(0.102)|
|Latin America & Caribbean|-.204<sup>_∗∗_</sup>|-.044|-.152|
||(0.089)|(0.109)|(0.125)|
|Middle East & North Africa|-.479<sup>_∗∗∗_</sup>|-.337<sup>_∗∗∗_</sup>|-.394<sup>_∗∗∗_</sup>|
||(0.112)|(0.128)|(0.11)|
|South Asia|-.468<sup>_∗∗∗_</sup>|-.457<sup>_∗∗∗_</sup>|-.474<sup>_∗∗∗_</sup>|
||(0.116)|(0.125)|(0.117)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.897|0.897|0.897|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

8
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reflection of a legacy which though institutionalized is showing signs of diminishing returns. The standardization of advice has become damaging.

With respect to the quality of information available to make the right technical and financial decisions, progress has been made and there are now comparable monitoring indicators of the commercial, financial, operational and technical performance of the sectors for a large number of operators. Most regions have now organized regional water regulators and operators’ associations. Moreover, initiatives such as the International Benchmarking Network for Water and Sanitation Utilities (IBNET) promoted by the World Bank, the OECD Water Governance Indicators or the more commerciallyoriented platforms, such as the Global Water Intelligence (GWI) or the International Water Association (IWA), all produce useful partial data sets allowing some degree of benchmarking. But none of them accounts for all of the relevant dimensions and most of them have an incomplete country or operator coverage. All these sources have their fair share of critics because the data collected are often incomplete or of poor quality.<sup>13</sup> But despite their limitations, they already provide a solid basis to conduct some of the necessary policy work. However, more and better data are needed.

The main information gaps concern costs which, in regulation, is a significant gap as it impacts the scope of accountability as discussed later. It results from a failure to adopt standard regulatory accounting practices which are essential to be able to compute tariffs, rates of return or financing requirements in a rigorous way which accounts not only for financial concerns but also for social concerns. This is an illustration of what technical and capacity constraints mean in practice. Lots of data are missing to be able to make the right regulatory decisions.

In many countries, this is the result of a wrong skill mix or level among the staff of the agencies and the ministries. It can also often be due to a lack of budget capacity needed to outsource or acquire technical assistance or put in place an information collection system which could be used to measure performance, forecast needs and assess the real financing and technological options at the city, regional or national level. It also reflects incomplete or uncertain mandates. And maybe just as importantly, it reflects a significant lag in adopting and or implementing the most basis tools to conduct fair cost and pricing diagnostics.

The main outcome is that the regulators are less likely to be able to observe costs and distinguish between the extent to which high costs are linked to the operators’ technological constraints, inefficiencies of information or their efforts to make the most of the technology available. This lack of access to correct cost information is what leads to increased firm rents, possibly lower supply and lower social welfare.

> 13 See van den Berg and Danilenko (2011) for a description of the IBNET data set.

7
derived from the bootstrapped estimates. This is a chi-square approximation to the distribution of the gain relative to the linear marginal relationship between the independent and dependent variables, and is rejected in 100 percent of the bootstrapped estimates, indicating that the relationships are significantly nonlinear.

# **_3_**<sup>**_rd_**</sup> **_-stage simulations_**

In order to understand the diverse preferences for dwelling unit attributes, we regress the structural utility estimates derived using the first-order conditions in (7) and (8) on a range of household characteristics. For the dichotomous variables, we follow Bajari and Kahn and estimate a probit regression on a dummy variable indicating possession of the attribute, while constraining the parameter on the price (obtained in the GAM) to –1. For the continuous variables, we run simple linear regressions on the rental value of an improvement in the attribute of one-half of one standard deviation around the mean. Table 2 shows the magnitudes of these changes. For the dichotomous attributes (mains water, sewer), the value of the improvement is clearly 100 percent of the value of the attribute. For the continuous variables, the simulated changes range from a seven percent improvement (ie a decline) in the share of neighbors who feel air is severely polluted, to a 100 percent increase in the number of parks within one kilometer. The range is large, since the changes are defined by the distribution of the underlying variables.

Other things being equal, the value of the tax in the price of housing is an indicator of the net benefit of local taxes and services to the local residents. A negative value would indicate that the net benefit of the tax and local services is negative; the tax

> 11 These are the means of the bootstrapped parameter estimates and variances.

20
auto generation capacity installed in 2009 (estimated in around 3,427 MW)<sup>2</sup> would already be above the capacity of the national grid (2,992 MW).

Where most of auto generation capacity is installed in industrial facilities and mines, private households are increasingly using electrical inverters and generators to respond to blackouts. Departing from 2001 data and supposing that imported electricity generation devices not exceeding 75 kvA are for household use, auto generation capacity at the household level would be of around 766 MW in 2009. A second alternative way to contrast electricity generation capacity privately installed for complementary use can be found by departing from ENHOGAR 2007. This survey asks whether the subject has an “inversor” or generator at home, and 18.1% of households in the Dominican Republic count with one. Supposing an average generation capacity of 1.5KW per household, total supplementary capacity installed for private household use in the Dominican Republic would be around 678 MW, well above the 53 MW estimated by the National Energy Commission in 2001. According to sector experts, the increase in private auto generation capacity may have been motivated by the impoverishment in the quality of energy supply following 2003 economic crisis.

Which is the cost of maintaining these auto generation devices? According to some estimates<sup>3</sup> , the charge needs of the inverter ́s battery represent 63% of the average electricity consumption by a middle-income household (453 KWh a month). Considering 3 million households registered in the 2010 census and supposing that around 18-20% of the households count with an “inversor”, it results that approximately 154 GWh a month<sup>4</sup> are consumed just to support individual private auto-generation capacity. This is a very significant cost of opportunity of inefficiency in the system, which burdens both the state and final consumers, who pay around 240 USD million of their annual electricity bill just to keep the inverter working. This approximation seems to be in line with previous estimations: _<<In 2005 – US$400 million was spent to install and maintain self generation for the commercial and industrial sector, and at least another US$150-200 million was spent on residential self-generation>>._ World Bank, 2009.

These figures highlight the existence of a large auto generation capacity, individually organized as a response to a malfunctioning public service. Large private investment here is likely to result inefficient, as alternative generation capacity is idle when the public grid is functioning. Private auto-generation capacity is believed to be at least equivalent to the

> 2 Refer to Annex II for an explanation of the calculations

> 3 EDEESTE, “Guía para ahorrar energía”, 2011.

> 4 We are just considering here households with inverters. To this figure it would be necessary to add electricity consumption derived from the use of generators.

[7]
Table 2: Earnings Functions: Level and Years

||199<br>|8<br>|200<br> <br>|2<br>|
|---|---|---|---|---|
||Estimated<br>Coefficient|Standard<br>Error|<br>Estimated<br>Coefficient|Standard<br>Error|
|Primary complete|0.028|0.025|0.021|0.035|
|Secondary complete|0.033|0.024|0.095|0.031|
|Tertiary complete|0.110|0.029|0.160|0.036|
|Years of primary|0.044|0.007|0.054|0.010|
|Years of secondary|0.089|0.008|0.077|0.010|
|Years of tertiary|0.137|0.008|0.153|0.011|
|Experience|0.042|0.002|0.040|0.002|
|Experience-squared|-0.001|0.000|-0.001|0.000|
|Constant|-0.379|0.041|-0.607|0.056|
|R<sup>2</sup>|0.283||0.282||
|N|14,485||9,309||

Source: EPH

# **Testing for Non-Linearities**

The methods used thus far may not account for the timing of the dropout decision. Another technique used is to look at year-to-year returns and examine if there are discontinuities in the estimates at years corresponding to the termination of levels. We estimate year-to-year returns to schooling in order to detect discontinuities at years corresponding to levels, or nonlinearities in the returns to education, which could be taken as evidence of “sheepskin effects” (see Annex Table 2). This method provides even weaker evidence of any credentialism. There do not appear to be significant increases associated with years of schooling that would represent the attainment of a primary or secondary certificate. The only signal that there might be credentialism occurs at 17 years of schooling which could be argued represents the attainment of a tertiary education degree. The implied step sizes according to levels of schooling indicate spikes at years 7, 12 and 17 for primary, secondary and tertiary (see Figure 2). Curiously, 15 years of schooling represents a significant “threshold” in 2002 (but not in 1998).<sup>2</sup>

> 2 Excluding _Jefes_ does not alter the conclusions.  In fact, the same step sizes are estimated.  Further, the steps at some non-credential years actually increase slightly (see Appendix A).

- 6 -
Here _remitting behavior_ is measured in three ways – as total remittances which includes zero for those who do not remit, as a binary indicator of whether or not an individual remits (the extensive margin), and as log of the amount remitted for those who remit (the intensive margin). Our aim through these regressions is to describe the characteristics of this remitting behavior, and to test whether there are differences in remitting behavior across origin countries after controlling for destination country survey fixed effects and for differences in observable characteristics across individuals.

There is now a rich theoretical literature which describes a variety of different reasons individuals remit (see Rapoport and Docquier, 2006). Our choice of which individual characteristics to control for is motivated by these theories, but it should be noted that our intent is not to test which of these theories best describes the remitting behavior of Africans, but merely to use these theories to help understand which characteristics might be associated with remitting behavior. The comprehensive dataset we have constructed contains a number of variables of interest, but is lacking in several key variables that would be needed to adequately test different theories of why people remit. For example, we do not have information on the income levels of the family members left in the home country, or on the shocks experienced by either the migrant or their family. This prevents attempts to tease out whether altruism or insurance motives are driving the results. Likewise we know whether remittances are being made, but not whether they are being used to repay a loan or to invest in property, which are other important reasons for remitting.

Nevertheless, we believe that the descriptive regression analysis is itself of interest, since many of the questions of remitting behavior in the policy arena are ones of a descriptive not a causal nature. Thus policymakers are interested in whether male or female migrants remit more or less, not whether gender has a causal impact on remitting behavior. Likewise, there is interest in whether high-skilled migrants (measured by either education or income earned) remit more or less, rather than in whether causing a migrant to stay in school longer would change that particular migrant’s remitting behavior. This is because migration policy questions generally concern who is migrating, which involves comparing different groups of individuals, rather than with what the effects would be of changing the characteristics of the same group of people who migrate.

12