rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Table A19. Schooling, skills, migration, and earnings for women in Cambodia</u>

||<br>|<br>**Median r**<br>|<br>**egressions**<br>|||**Mean re**<br>|**gressions**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|
|Years of schooling (a1)|2.27|5.00***|3.65*|1.99|7.57|9.21|7.39|8.35|
||(1.75)|(1.72)|(2.11)|(2.43)|(9.11)|(10.3)|(10.7)|(10.0)|
|Cognitive skills (a2)|||2.23|2.43|||6.26|8.92|
||||(3.34)|(3.34)|||(13.3)|(13.6)|
|SEM skills (a3)|||7.44***|7.31**|||18.5|19.6|
||||(2.52)|(2.87)|||(13.2)|(13.3)|
|Out Village (a4)||||-32.7||||26.9|
|||||(78.6)||||(371.6)|
|Interaction YrsSchooling and Out Village (b1)||||4.02<br>(12.2)||||-5.46<br>(53.4)|
|Interaction SEM and Out Village (b2)||||14.1||||-19.8|
|||||(14.8)||||(28.2)|
|Interaction Cog and Out Village (b3)||||13.7<br>(24.4)||||-38.0<br>(38.7)|
|Constant|163.7|141.1|167.1|215.1|-243.1***|-244.5***|-223.7***|-235.6***|
||(602.3)|(576.4)|(472.3)|(1248.2)|(56.8)|(60.7)|(66.5)|(65.6)|
|Observations|1642|1354|1354|1354|1642|1354|1354|1354|
|R-squared|0|0|0|0|-0.0054|-0.051|-0.050|-0.053|
|<br>Median/Mean dependent|123.1|123.8|123.8|123.8|208.7|205.0|205.0|205.0|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|a1+b1=0||||6.01||||2.90|
|||||(12.10)||||(52.82)|
|a2+b2=0||||<br>16.16||||<br>-29.09|
|||||(25.02)||||(38.10)|
|a3+b3=0||||21.43||||-0.17|
|||||(14.49)||||(29.03)|

* p<.10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, migration, and earnings for women in Cambodia. The dependent variable “monthly earnings” in columns (1) to (4) is the raw monthly earnings; the dependent variable “monthly earnings (TC)” in columns (5) to (8) is top-coded at 2,000 USD per month. The cognitive index is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computeradaptive test. The socioemotional (SEM) skills index is computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The “out of village” variable is a dummy variable that indicates whether the respondent lives outside the village where we originally surveyed their household. The sample for columns (1) and (5) is all women in the sample who are working and are not currently enrolled (1,642 women). For the rest of the columns, the sample is only those who have skills and location measures (1,354 women).

104
micro, meso, or macro level, which each measure and define worker productivity in their own way. Micro studies focus on observed individual worker performance, output or wages as the left-hand side variable.<sup>xxxii</sup> Studies at the sector or economy wide level typically consider output per worker, using an aggregate economic indicator such as output, value-added, or GDP, divided by reported number of workers employed.xxxiii Increased temperature generally has an adverse effect on labor productivity, and this seem to hold across settings, specifications, levels of observation, and types of output. This is confirmed by a recent review of a subset of studies (Lai et al., 2023). Micro-level analysis in developing countries finds a negative relationship between productivity and temperature. In India’s manufacturing sector, a 1<sup>_◦_</sup> C increase on a hot day reduces labor productivity by 2–4% (Somanathan et al., 2021). For garment factories in India, productivity effects of relatively hot days are in the magnitude of -2 pp/+1<sup>_◦_</sup> C for temperatures above 27<sup>_◦_</sup> C-28<sup>_◦_</sup> C (Adhvaryu et al., 2020). Workers’ data from non-climate-controlled manufacturing firms in China suggest a U-shaped relationship: temperature is positively correlated with productivity up to 78<sup>_◦_</sup> F (~25<sup>_◦_</sup> C), after which productivity declines with further warming (Cai et al., 2018). Call center workers in India see a productivity decline of 1.8% for a 1<sup>_◦_</sup> C rise over a temperature range from 21 _._ 9<sup>_◦_</sup> C to 28 _._ 5<sup>_◦_</sup> C (Niemela et al., 2002).

Because individual worker productivity can be hard to measure, some research focuses on the change in firm Total Factor Productivity (TFP) obtained from estimating a production function. Firm-level analysis of 500,000 Chinese manufacturing plants finds heat stress to be strongly related to economic output (Zhang et al., 2018): a day with a temperature above 90<sup>_◦_</sup> F (32<sup>_◦_</sup> C) lowers TFP of the average manufacturing firm by 0.56% relative to a day with a temperature between 50◦ -60◦F (10<sup>_◦_</sup> C-15<sup>_◦_</sup> C), resulting in 0.45% lower output. Productivity losses tend to be larger for workers in heat-exposed industries.<sup>xxxiv</sup> In Chinese manufacturing, the largest losses in value added per worker on hotter summer days occur in industries where workers are highly exposed to heat while air conditioning is largely infeasible; these include ferrous 15
|General Charateristics|Firms<br>(%|
|---|---|
|Sige -Mera (0-5 emplovecs}|ag|
|Age-<br>Tdveers orless||
|Level of education oflargest owner -No Education|6|
|Largest ownerhaz ajobin aformal business<br> <br>|8<br>|
|Use ofFinance<br>|Po|
|Use of accounts|2a|
|Use ofloans|11|
|Working capital finance -Jnternal funds, familyand money lenders|80|
|Investmentfinance - Internal funds, famulyand money lenders<br><br>|54<br>|
|Business Registration<br>|Po|
|Registered at startup|4|
|Like to register|5g|
|Mainreasonfornotregistering - Texes|26|
|Mostimportantbenefitfromregistering-Accesstofinence|52|
# **Introduction**

Although the rise of China and India in the global economy cannot be ignored, their impact on the development prospects of other developing countries is difficult to identify. The emergence of these Asian economies in world markets is seen as an opportunity by some analysts and as a threat by others. This paper studies the relationship between the rapid growth of China and India in world trade and Latin American and Caribbean (LAC) commercial flows from two perspectives: first, from the viewpoint of China and India as fast-growing export markets and as sources of imports for LAC, and second, in terms of their potential effects on LAC trade flows with other markets.

The economic accomplishments of these Asian economies have been extraordinary. During the past two decades China and India increased their share of global GDP from 3 to 7 percent. China is currently the sixth and India the tenth largest economy in terms of GDP. The growth of China and India was accompanied by their rapid integration into world markets. China is currently the third largest trading economy in the world (behind the United States and Germany), while India ranks twenty-fifth.

These trends can be seen as an opportunity for other developing countries. For example, China and India became the third trading partner of the LAC region, and with a growth rate of their demand close to 9 percent over the last two decades, the future potential looms large. The importance of China and India as destinations for LAC exports increased four-fold since 1990, when they represented around 1 percent of LAC exports. Furthermore, during 2000-2004, LAC non-fuel merchandise exports to China grew by an average annual rate of over 40 percent (in current US$), while exports to India grew by 25 percent.<sup>1</sup> These rates of export growth signal significant opportunities, even though the levels remain low, representing less than 10 percent of total exports for most LAC economies (see Figure 1). Similarly, the share of China and India in total LAC imports increased significantly over this period (see Figure 2).

> 1 The rate of growth of non-fuel merchandise exports to China and India was calculated with data from WITS/UNCOMTRADE data in current US dollars covering the following sample of countries during 20002004: Argentina, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominica, Ecuador, Guatemala, Guyana, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, St. Lucia, Trinidad and Tobago, Uruguay, Venezuela.

1
Table 5. Consumptive water use for 2000 and 2050 in km<sup>3</sup> per year. IMPACT model simulations (Rosegrant et al., 2009).

||**Dome**|**stic**|**Indus**|**trial**|**Livest**|**ock**|**Irriga**|**tion**|
|---|---|---|---|---|---|---|---|---|
||**2000**|**2050**|**2000**|**2050**|**2000**|**2050**|**2000**|**2050**|
|**CWANA**|11|31|6|16|12|19|489|420|
|**ESAP**|64|153|48|134|16|24|1,256|1,259|
|**LAC**|15|30|7|14|6|9|224|324|
|**NAE**|41|48|91|110|6|5|598|615|
|**SSA**|7|45|1|7|4|7|50|88|
|**Developed**|45|51|94|111|7|6|606|623|
|**Developing**|93|256|59|169|38|58|2,010|2,085|
|**Globe**|138|307|153|280|44|63|2,616|2,707|

CWANA Central and West Asia and North Africa ESAP East and South Asia and the Pacific LAC Latin America and the Caribbean NAE North America and Europe SSA Sub-Saharan Africa non-point source pollution from agricultural practices. Several countries in the ESAP region suffer from this type of water scarcity.

Substantial increases in non-irrigation water demands are projected over the next 50 years, with increases concentrated particularly in developing countries (Table 5). For example, domestic water consumption in developing countries is projected to increase from 93 km<sup>3</sup> to 256 km<sup>3</sup> by 2050. This increase is driven by both population growth and per capita demand increase arising from income growth.

One of the major uses of water in relation to livestock (indeed, in relation to agriculture in general) is in the production of livestock feed, particularly when compared with drinking by and servicing of animals, and product processing (Steinfeld et al., 2006). Increasing livestock numbers in the future will clearly add to this demand for water. At the same time, Peden et al. (2006, 2007) note that the key entry point for improving global livestock water productivity is strategic sourcing of animal feed, an area of research that has largely been ignored to date. The amount of evapotranspired water needed to produce 1 kg of dry animal feed is highly 24
Table 3: Intergenerational Persistence in Schooling in Indonesia (with Controls for Cognitive Ability)

|||**LINEA**|**R CEF**|||**QUADRA**|**TIC CEF**||
|---|---|---|---|---|---|---|---|---|
||**Full**|**Rural (R)**|**Urban (U)**|**R–U**|**Full**|**Rural (R)**|**Urban (U)**|**R–U**|
|Father Edu|0.378***|0.352***|0.395***|-0.043**|0.226***|0.184***|0.288***|-0.104**|
||(0.010)|(0.012)|(0.014)|(0.017)|(0.023)|(0.025)|(0.038)|(0.042)|
|Father Edu Sq|||||0.011***|0.013***|0.006***|0.006***|
||||||(0.001)|(0.002)|(0.002)|(0.002)|
|Constant|5.416***|5.346***|5.753***|-0.407**|5.789***|5.7246***|6.0720***|-0.347|
||(0.155)|(0.173)|(0.188)|(0.198)|(0.170)|(0.184)|(0.225)|(0.225)|
|Ability Index|0.464***|0.536***|0.322***||0.477***|0.542***|0.336***||
||(0.046)|(0.052)|(0.064)||(0.046)|(0.052)|(0.064)||
|Ability Index Sq|-0.021***|-0.028***|-0.007||-0.022***|-0.029***|-0.009||
||(0.004)|(0.004)|(0.006)||(0.004)|(0.004)|(0.006)||
|Observations|15477|10239|5238|15477|10239|10239|5238|15477|

||**Relative**|**Estimates**<br>**Mobility**|**of Relative and Ab**|**solute Mobility from**|**Quadratic**<br>**Absolute**|**CEF**<br>**Mobility**||
|---|---|---|---|---|---|---|---|
||**Full**|**Rural(R)**|**Urban(U)**||**Full**|**Rural(R)**|**Urban(U)**|
|**IGME0**|0.2262|0.1838|0.2880|**ES0**|5.7887|5.7246|6.0720|
|**IGME6**|0.3522|0.3362|0.366|**ES6**|7.5239|7.2846|8.034|
|**IGME9**|0.4152|0.4124|0.405|**ES9**|8.675|8.4075|9.1905|
|**IGME12**|0.4782|0.4886|0.444|**ES12**|10.0151|9.759|10.464|
|**IGME16**|0.5622|0.5902|0.496|**ES16**|12.0959|11.9166|12.344|
|**Weighted IGME**|0.3588|0.3275|0.3856|**Weighted ES**|7.7738|7.326|8.704|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Here, _IGMEk_ = _ψ_<sup>ˆ</sup> 1 _j_ + 2 ˆ _ψ_ 2 _jEkp_<sup>and</sup><sup>_ESk_=</sup><sup>_ψ_ˆ0</sup> _j_ + _ψ_ ˆ1 _jEkp_<sup>+</sup><sup>_ψ_ˆ2</sup> _j_ ( _Ekp_<sup>)2where</sup> _E_<sup>_p_</sup> is father’s years of education. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square.

35
to insure consumption).

The rest of the paper is sectioned as follows. In Section 2, we provide a full account of the data construction. In Section 3, we discuss in detail the distributional facts of CIW for Malawi, Tanzania, and Uganda, with more emphasis on Malawi. The emphasis on Malawi is due to the sample size being four times larger and the data more detailed than for the other two countries. The corresponding tables for Tanzania and Uganda are available in the online appendix. In Section 4, we decompose the consumption basket, the income sources, and the wealth portfolio in rural and urban areas. In Section 5, we investigate the sources of inequality and identify pockets of the population with positive savings. In Section 6, we explore economic mobility using panel information for Uganda in 2005 and 2009. We conclude in Section 7.

# **2 ISA Data and Measurement Issues**

The Integrated Surveys on Agriculture (ISA) are conducted under the umbrella of the Living Standards Measurement Study (LSMS). ISAs greatly improve previous LSMS household surveys in use for decades. In particular, ISAs are unique in the level of detail in CIW for households that — make their living through agriculture. The ISAs allow us to recover for any practical purpose —the entire deseasonalized household-level budget constraint in a set of the poorest countries of SSA.<sup>7</sup> However, in addition to the ISA improvements in data collection and availability, a correct measurement of CIW requires further adjustments. In particular, due to the importance of agricultural production, estimating the monetary value of income and consumption requires us to convert physical units in which production and consumption are reported into a single unit, assign prices to items that are produced by the household for own consumption, and deseasonalize consumption. As we show, the choice of which price to use to value non-sold production — either — the price at the gate (i.e., production price) or consumption price can have large impacts on the estimated monetary values of agricultural production.

## **2.1 Units Conversion of In-Kind Items: From Pails to Kilograms**

In household surveys from poor countries, it is standard to report amounts of consumption (including from own production), production (mainly agricultural) and additional sources of income (e.g., gifts and transfers) in units that are not necessarily harmonized across time or space. For example, in the Malawi ISA, households are asked to report the amount they produce of a given item in any unit they wish, and this varies from bags, dishes, bunches, and pails, to kilograms

> 7To the best of our knowledge, the availability of the CIW triplet is rather unique as these three items are not supplied at the same time by any other single dataset, not even in developed countries. See also the discussion in Heathcote et al. (2009).

6
**Teacher shortages.** The probability of hiring the teacher in a high management private school is higher than the probability of hiring the teacher in an intermediate management public school (via teacher selection). In the numerical example shown in the top panel of Figure 7, the area of the grey region is bigger than the area of the unshaded region.

The PISA dataset does not contain objective information on school-level vacancies, so we use a series of 4 questions in the school principal questionnaire that ask the principal whether he/she feels that the school’s capacity is hindered by a lack of qualified teachers in each of math, science, language and ‘other subjects’.<sup>22</sup> It is worth emphasising that these questions are open to considerable interpretation. For instance, a principal might answer ‘a lot’ because he/she feels that the school needs more new posts even if there are few vacancies for existing posts. Conversely, he/she might answer ‘not all’ because of a belief (or desire to say) that the school is coping despite there being vacancies.<sup>23</sup> With this caveat in mind, it can still be instructive to examine the data. Column (1) of Table 3 shows that, consistent with the theory, the teacher shortage index is 0 _._ 535 standard deviations _lower_ among private schools than public schools, significant at the 1 percent level. Column (4) repeats the specification but with the people management index instead of the private school dummy. The relationship is consistently negative, suggesting that a one standard deviation increase in the people management index is correlated with 0 _._ 062 _lower_ teacher shortage in schools, marginally significant at the 10 percent level.

**Teacher motivation.** The expected intrinsic motivation of a teacher hired to a high management private school is higher than the expected intrinsic motivation of a teacher hired to an intermediate management public school (via teacher selection and augmentation of teacher intrinsic motivation). In the numerical example in the top panel of Figure 7, the vertical height of the black point is greater than the vertical height of the blue point.

We explore this prediction by using the school climate section of the school principal questionnaire

> 22We describe how this teacher shortage index, and the other intermediate outcome indices for teacher motivation, teacher effort, and household involvement, are constructed in Appendix C. All indices are standardized.

> 23Consistent with this, by far the most common answer given by principals in both sectors is ‘not at all’, as reflected in the density of the standardized score in Figure B.5 in Appendix B.

20
We use preliminary estimates of debt relief from this new dataset to study the related question posed in the title of this paper: Who gets debt relief? In Section 3 of the paper we examine the allocation of debt relief across a sample of 62 low-income developing countries to study how the determinants of the incidence and size of debt relief differ from other forms of aid. While foreign aid is significantly higher in poor countries, we find little evidence that debt relief disproportionately benefits poor countries. We find some evidence that debt relief, particularly from multilateral creditors in recent years, has been significantly higher for countries with good policies and institutions. Given the emphasis in recent years on improving the "selectivity" of foreign aid in order to ensure that it goes to countries where it is likely to have the highest impact, the finding that debt relief has also been allocated selectively is encouraging.<sup>4</sup> We find similar patterns of increased selectivity in foreign aid, confirming existing results in the literature. Interestingly, however, we find that conditional on policy and institutional performance, as well as per capita incomes, we find only weak evidence that debt relief goes to countries with higher levels of indebtedness, and no evidence that it goes to countries that have displayed difficulties in servicing their debts through the accumulation of arrears. This suggests that alleviating debt burdens per se may not in fact be the primary motivation for debt relief. Finally, our cross-sectional results provide suggestive, but not conclusive, evidence that large debtor countries (in the sense of accounting for a large share of the portfolio of lenders) are somewhat more likely to receive debt relief.<sup>5</sup> In the final section of the paper we study the time series properties of debt relief using dynamic panel probit regressions where the dependent variable is an indicator variable measuring the incidence of debt relief. As we document below debt relief exhibits considerable persistence within countries over time: one-third of the 62 lowincome countries in our sample account for over 70 percent of the debt relief episodes that we identify. We examine whether this persistence in debt relief is due to serial

> 4 This is also consistent with the stated structure of the HIPC initiative, under which debt relief is granted conditional on good policy performance.  Similar requirements were also in place for earlier rounds of debt relief through the Paris Club, recognizing that debt relief differs from other forms of aid in that it constitutes untied budget support and thus is likely to be better used in countries with good policies and institutions.  However, we find little empirical evidence of policy conditionality for debt relief in the earlier part of our sample.

> 5 This finding is related to the results of Birdsall, Claessens and Diwan (2003) who show that net transfers are higher in more highly-indebted countries in Sub-Saharan Africa during the 1980s and 1990s.

2
|Inequality Index|(4)|-0.008<br>[0.002]***|Yes<br>Yes<br>-0.131<br>[0.043]***<br>Yes<br>Yes<br>but includes additional city|the city's share of GDP in|ty.|
|---|---|---|---|---|---|
|Unemployment<br>Rate|(3)|0.003<br>[0.001]***|Yes<br>Yes<br>0.076<br>[0.022]***<br>Yes<br>Yes<br>specifications as in table 6|DP composition includes|ts and av. firm size in the ci|
|Share Informal<br>Workers<br>Poverty Rate|(1)<br>(2)<br>Panel A: OLS|-0.013<br>-0.008<br>[0.002]***<br>[0.001]***|Yes<br>Yes<br>Yes<br>Yes<br>-0.132<br>-0.058<br>[0.040]***<br>[0.022]***<br>Yes<br>Yes<br>Yes<br>Yes<br>Panel B: IV<br>icant at 5%; *** significant at 1%. Table reports the same|s well as firm and worker characteristics. City sector G|haracteristics include av. age, share females, share migran|
|Dependent Variable:||Log Inspected Firms per firm in city|City Sector GDP composition<br>City Firm and Worker characteristics<br>Observations<br>Log Inspected Firms per firm in city<br>City Sector GDP composition<br>City Firm and Worker characteristics<br>Observations<br>Standard errors in brackets, * significant at 10%; ** signif|level controls to capture the city's sector composition a|agriculture, industry and services. City firm and workers c|
# **Figure B25: Coffee, Robusta**

<!-- Start of picture text --> Log scale<br>3.5<br>Log price Permanent component<br>2.5<br>1.5<br>0.5<br>-0.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 66 —_