rafmacalaba/datause-encoder-data huggingface dataset:
Annex 6. Fixed effects model with instrumental variable: the effect of the FAIS on poverty, by population size A. Poverty, by population size

|||_Urban_|||_Semiurban / rural_||
|---|---|---|---|---|---|---|
||_Food_|_Capabilities_|_Assets_|_Food_|_Capabilities_|_Assets_|
|FAIS ∞|−3.176***|−2.577**|−0.429|2.337|2.912**|4.084***|
||(1.139)|(1.151)|(1.066)|(1.440)|(1.427)|(1.280)|
|Own revenues ∞|−0.295|−0.420|−0.484|−0.317|−0.238|0.0402|
||(0.436)|(0.430)|(0.368)|(0.341)|(0.322)|(0.244)|
|Unconditional revenue ∞|1.326|0.821|−0.839|−2.574**|−3.212***|−4.181***|
||(0.965)|(0.973)|(0.859)|(1.206)|(1.221)|(1.136)|
|Conditional revenue ∞|0.529|0.544|0.657|0.534|0.661|0.668|
||(0.533)|(0.534)|(0.475)|(0.584)|(0.594)|(0.554)|
|Direct workers ‡|−17.56*|−14.03|3.461|−10.29|−12.85|−15.40|
||(10.27)|(11.19)|(12.32)|(9.308)|(10.12)|(10.07)|
|Indirect workers ‡|30.68**|27.67**|15.57|−5.150|−7.324|−10.19|
||(12.08)|(12.86)|(13.71)|(13.65)|(15.65)|(16.10)|
|Productivity per occupied ‡‡|0.210|0.228|0.346|0.681|0.755*|0.709*|
||(0.481)|(0.488)|(0.427)|(0.448)|(0.447)|(0.391)|
|Private business assets ‡‡|−0.334|−0.353|−0.420|−0.268|−0.283|−0.245|
||(0.359)|(0.370)|(0.337)|(0.406)|(0.401)|(0.335)|
|Year 2010|1.078***|2.110***|4.304***|0.308|0.457|1.016**|
||(0.384)|(0.395)|(0.374)|(0.522)|(0.527)|(0.462)|
|Year 2014|−0.493|−0.0211|0.630|−3.021***|−3.415***|−3.565***|
||(0.459)|(0.462)|(0.405)|(0.573)|(0.585)|(0.527)|
|N|2,843|2,843|2,843|3,264|3,264|3,264|
|R-2|0.017|0.029|0.121|0.034|0.040|0.050|
|Municipalities|969|969|969|1,149|1,149|1,149|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010–14, also using the FAIS formulated as the instrumental variable.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 36
63

# **5.** ZIMBABWE: ELECTRICITY PRICE INCREASES **1983384**

# **Introduction**

Zimbabwe is a landlocked country sitated in the center of southem Africa covering a total area of 391 ,000 square kilometers. It has a total population of 9.8 rnillion and GNP per capita of US$ 640, placing it amongst the low middle-income countries in Africa.<sup>4</sup> '

# **The** Economy

Zimbabwe is a relatively industrialized African country with an annual GDP growth rate of 2.9 percent during the 1980s. At te time of independence in 1980, it inherited a well-diversified economy with yearly average growth rates of over5 perct durng 196540, although toward the end of this period, from 1975 onward, the economy had declined by<sup>3</sup> percent, mainly as a result of the civil war and unusu2I droughts. The country started with a developed modem sector with industry (25% of GDP in 1979) and commercial agcultue (1 I%). The country also inherited an extremely skewed distribution of income in favor of white households.

The 1980s started with implementation of the Growth with Equity programL Elements of the program included liberalization offoreign exchange, increase in agriculture producer prices and increased investment in transport infrastructure. These elements were based on the govemment's expectations of expansion **in** exports and increase in concession **a** l ssistane The equity part of the program involved rapid expansion in education and health services, increase in minimum wages and agricultural resettlement schemes. These policies, combined with favorable weather conditions and growth an foreign assistance, resulted in a sharp economic recovery in the country with GDP growth of nearly 10 percent in 1981.

However, the boom in 1980-81 was short lived. Growth in exports was much lower than expected, as traditional mining and agriculture exports did not expand. Manufactred exports also did not respond as expected. Shortfall in exports was a resut of a number of factors, among them an increase in donestic demand, loss in intenaional competitiveness due to domestic wage increases and appreciation of excages rates and the general world recession. In addition, concessional foreign assistance was delayed, resulfing in the governmenrts increased dependence on high-cost short-tenn commercial borrowing. As a result the current account deficit increased from Z$ 158 mil. in ,'80 to Z$ 703 mil. in 1981. By 1982 a 41 Data for 1990.
**_Table 4: Labor productivity convergence regressions conditioning on initial poverty level, Secondary sector_**

|||Secondarysector<br>||Mfi|NMf|
|---|---|---|---|---|---|
||Total<br>aggregation|Disaggregated by<br>Manufacturing &<br>NonManufacturing|Disaggregated<br>by all sectors|anuacturng<br>only|onanu.<br>only|
||(1)|(2)|(3)|(4)|(5)|
|Dependent variable:||||||
|Growth of VAper worker over a decade||||||
|Log initial VA per worker|‐0.026***|‐0.026***|‐0.017|‐0.039***|‐0.021*|
||(0.008)|(0.006)|(0.011)|(0.007)|(0.011)|
|Log initial poverty level|‐0.007|‐0.011|0.016|‐0.033*|‐0.003|
||(0.017)|(0.013)|(0.022)|(0.017)|(0.027)|
|Constant|0.275***|0.268***|0.201**|0.372***|0.233**|
||(0.070)|(0.061)|(0.095)|(0.064)|(0.102)|
|Observations|24|48|72|24|24|
|R‐squared|0.372|0.256|0.075|0.596|0.157|
|Stardard errors in parenthesis.||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||

When we estimate separate regressions for manufacturing and non‐manufacturing (the aggregation of water and electricity, and construction), we find some evidence that the convergence rate is higher within the group of manufacturing industries (3.2% versus 2.1%, Columns 6‐7 of **Table 3** ). The estimate implies that the departments in the bottom 25% of productivity in manufacturing grew faster than the top 25% most productive departments by 4.8 percentage points per annum (that is, 3.2 times their difference in log of initial productivity, which is approximately 1.5 points). **Table 4** shows very similar results once controlling for initial poverty. However, this point estimate of the manufacturing sector’s convergence rate is sensitive to dropping outlier departments (Lima‐Callao and Moquegua). The point estimate for manufacturing convergence drops to 2.3% after dropping these departments.

# **4.3 Absence of unconditional convergence in the tertiary sector**

As shown in Figure 4, departments with low initial labor productivity in the tertiary sector did not experience faster growth in the same period. Indeed, this figure suggests that Peru’s regions are diverging in terms of labor productivity in the tertiary sector.

14
defined in terms of broad social group membership (Scheduled Tribe, Scheduled Caste, and Other). Mutatkar (2005) finds a between-group contribution of less than 5% in three rounds of National Sample Survey data during the 1980s and 1990s (corresponding to 1983, 1993/4 and 1999/0). These findings hold irrespective of whether one looks at rural or urban areas. Deshpande (2000) finds an even lower contribution between these three social groups within the state of Kerala, using data from 1993/4 round of the NSS<sup>ii</sup> .

Part of the reason for this is that the inherent properties of standard inequality decomposition measures tend to be structured so as to understate between-group inequality. A recent paper by Elbers, Lanjouw, Mistiaen and Özler (2008) – hereafter referred to as ELMO (2008) - points out that the standard procedure for decomposing inequality into a between- and a within-group component fails to capture a particular feature of group differences that might be highly relevant to an assessment of their importance. We outline the mathematics of this below, but the intuition behind ELMO is quite simple: standard decomposition procedures assess the extent to which group divisions contribute to inequality by giving everyone within a group the average income of the group and then asking how much of overall inequality can be attributed to the inequality accounted for by the inequality in these group-average income levels. By comparing group-average income inequality against total inequality, the procedure in effect compares observed group differences against the extreme benchmark where each individual in the data is treated as a separate group. As a result, between-group inequality is always rather low, in comparison with the benchmark. In addition, standard measures have the mathematical property that between-group inequality will increase (or more precisely - never decrease) with a greater number of groups. This makes it very difficult to make comparisons across populations which have different numbers of groups within them. For instance, standard measures would likely report a rise in between-racial group inequality in the US when comparing census data from before 2000 to 3
Figure 5: Test for discontinuity of selected covariates: survey data <!-- Start of picture text --> Ln(km to road) Ln(km to major city)<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Ln(members with rights to comm. land) Ln(poverty index 2010)<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Ln(average slope in deg) Mean canopy cover 2000<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>Household head educ: secondary school or above Household depends upon subsistence agriculture<br>2011-2012 2013-2014 2011-2012 2013-2014<br>-10 -5 0 5 -15 -10 -5 0 5 10 -10 -5 0 5 -15 -10 -5 0 5 10<br>2 1<br>1<br>0 0<br>-1<br>-1<br>-2<br>de-meaned value de-meaned value<br>-3 -2<br>3 2<br>2 1<br>1<br>0<br>0<br>-1 -1<br>de-meaned value de-meaned value<br>-2 -2<br>1 50<br>0<br>0<br>-1<br>de-meaned value de-meaned value<br>-2 -50<br>.1 .1<br>.05<br>.05<br>0<br>0 -.05<br>de-meaned value de-meaned value<br>-.05 -.1<br><!-- End of picture text -->

> 1 Figures plot the variables of interest from which we subtracted their state-level means, against the re-centered applicant point scores.

29
11 impact on uncertainty about future exchange rate crises as the dominant factor in explaining the investment and growth response to debt relief. An important qualification follows also, however, almost as a corrollary; these secondary effects through reduced uncertainty will obviously not come into play unless other, potentially dominant sources of future policy uncertainty have been removed first. Thus the successful domestic reform program that the Mexican government put through in the years nreceding the debt package was a necessary precondition for it to be successful.

# IV. Is arowth sustainable after the Brady deal?

The longer maturities and increased share of fixed-interest debt that the Brady deal brought about, along with the larger share of risk capital that is associated with high foreign investment in the post-Brady deal era, help to reduce Mexico's vulnerability to a potential crisis. However, much of the inflow is clearly a stock adjustment rather than a continuing flow: the privatization program is basically over, and Mexicans can bring their money back in only once. This raises the issue of sustainability once again, the more so because the whole process has been accompanied by an unprecedented deterioration in Mexico's current account balance. Mexico's current account deficit balance declined from a US$3.8 billion surplus in 1987 to an annual US$24 billion deficit on average in 1992-93 (Figure 6).

A simole calculation based on recent patterns of saving, investment and growth indicate that, at 1993 levels of saving, the 1990-92 rates of growth were not sustainable. For the economy to grow at 3.6%, the average growth in 1990-92, given an average ICOR (incremental capital-output ratio) of 5.8 for the same period, aggregate investment ought to be around 21% of GDP (3.6 times 5.8)Based on the most recent domestic saving figures, 16% of GDP during 1993 (using preliminary constant price national accounts data), achieving the above investment rate requires foreign saving of about 5% of GDP. For growth to be
output of agricultural enterprises versus household farms where the latter production passed from 45% to 74% of total agricultural production between 1995 and 2000.<sup>21</sup> Also noteworthy is the fact that our four different categories have different degrees of internal stability (the values of the diagonal of the Markov transition probability matrix) and that this degree changes over the period. Between 1998 and 1999 (recession year), the most stable category is NAE followed by AE, F and SE. At the end of the growth period we consider (1999-2002), the most stable category is still NAE but this is now followed by F while AE has almost halved its degree of stability and SE has more than doubled it.

In fact, wage workers (W=AE+NAE) are much more stable as a category than Non Wage workers (NW=F+SE) in 1998/1999 but this stability decreases over the period 1999-2002 from 75% to 61% while the degree of stability of non-wage workers almost doubles. Most of the growth in W instability is explained by the flow of NW into W which significantly decreases over the period accompanied by a growth of the reverse flow from W to NW workers.

**Table 4.5 Transition Probabilities between Labor Market Statuses**

||**Recession **|**Growth **|**Growth **|**Growth **|
|---|---|---|---|---|
|AE-AE|**98/99**<br>32|**99/00**<br>26|**00/01**<br>24|**01/02**<br>17|
|NAE-AE|28|30|23|14|
|F-AE|25|24|22|16|
|SE-AE|38|19|19|20|
|AE-NAE|41|43|43|42|
|NAE-NAE|49|39|47|48|
|F-NAE|50|49|41|39|
|SE-NAE|42|47|39|40|
|AE-F|23|26|25|37|
|NAE-F|17|25|26|31|
|F-F|18|23|32|39|
|SE-F|16|26|39|30|
|AE-SE|5|6|8|4|
|NAE-SE<br>|6<br>|6<br>|5|7<br>|
|F-SE|6|4|5|6|
|SE-SE|4|8|3|10|
|W-W|75|68|68|61|
|NW-W|77|71|62|56|
|W-NW|25|32|32|39|
|NW-NW|23|29|38|44|

Source: Moldova HBS 1998-2002. Legenda: F= Farmer; AE=Agricultural Employee; NAE=Non-Agricultural Employee; SE=Self-Employed (non-farmer); W=Wage workers (AE+NAE); NW=Non Wage workers (F+SE) So, why have people continued to move into private farming and into the non-wage sector during the recent growth period? The two possible answers can be classified as a 'pull' factor, i.e. private farming offers better income opportunities or as a 'push' factor, i.e. private farming is only an escape from unemployment and poverty. To test these two hypotheses we use the Moldova HBS panel 1998-2002 and split the sample of workers into wage and non-wage workers and into 'stayers' and 'movers' between the two categories defining four groups: Stayers into wage, movers from wage to non wage, stayers into non-wage and movers from non wage to wage. We then run a probit regression between stayers and movers in the two wage and non wage groups. We use a

> 21 http://www.statistica.md/

30
Table 7: Correlation Between Manager Quality and Employees’ Perceptions of Pollution as a Problem

||(1)<br>Concerned<br>Poll Planet|(2)<br>Concerned<br>Poll Planet|(3)<br>Concerned<br>Poll Health|(4)<br>Concerned<br>Poll Health|(5)<br>Gov<br>Address Poll|(6)<br>Gov<br>Address Poll|(7)<br>Ideal Job<br>Low Poll|(8)<br>Ideal Job<br>Low Poll|
|---|---|---|---|---|---|---|---|---|
|Median Road Size/Cell|0.0511<sup>_∗_</sup>|0.0518<sup>_∗_</sup>|-0.00558|-0.00569|0.00252|0.000615|0.0252<sup>_∗_</sup>|0.0281<sup>_∗∗_</sup>|
||(0.0284)|(0.0286)|(0.0376)|(0.0368)|(0.0335)|(0.0334)|(0.0131)|(0.0127)|
|Man. Score|0.274<sup>_∗∗∗_</sup>|0.254<sup>_∗∗∗_</sup>|0.227<sup>_∗∗∗_</sup>|0.211<sup>_∗∗∗_</sup>|0.0897<sup>_∗∗_</sup>|0.0957<sup>_∗∗_</sup>|0.0693<sup>_∗∗∗_</sup>|0.0603<sup>_∗∗∗_</sup>|
||(0.0327)|(0.0319)|(0.0476)|(0.0462)|(0.0446)|(0.0415)|(0.0151)|(0.0146)|
|N|2045|2045|2044|2044|2045|2045|2045|2045|
|R2|0.167|0.188|0.187|0.204|0.123|0.135|0.133|0.161|
|Fixed Efects (1)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Fixed Efects (2)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Level of Observation|Employee|Employee|Employee|Employee|Employee|Employee|Employee|Employee|
|SE clustering|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|
|Employee Controls|No|Yes|No|Yes|No|Yes|No|Yes|
|Mean(dependent var)|3.964|3.964|3.735|3.735|4.043|4.043|.298|.298|
|Answer scale|1-5|1-5|0-5|0-5|1-5|1-5|Dummy|Dummy|

Notes: Standard errors are displayed in parentheses (* p _<_ 0.10, ** p _<_ 0.5, *** p _<_ 0.01). Man. Score is a standardized index of managerial ability constructed using our survey (see Appendix A.1 for details). We control for log distance to the main city in the region and whether the grid cell contains any road. We also control for whether the grid cell is incomplete (i.e., _<_ 500m x 500m), its area, as well as a dummy for whether it is in our main surveyed area. Employee controls include education, age, age squared, vocational training (dummy), cognitive ability, employee tenure and log wage. We control for missing managerial score (dummy) and missing employee controls (dummy). Road size goes from 1 (Trail/Track) to 5 (Highway). The dependent variables are defined as follows: the employee is asked how concerned she is about the effects of air pollution on the health of the planet (cols 1, 2); to what extent she is concerned about the effects of air pollution on her own health (cols 3, 4); to what extent she agrees that the government should do more to promote and encourage a better air quality even if her taxes have to go up slightly (cols 5, 6); and whether her ideal job features low levels of air pollution (cols 7, 8).
Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 65-108.

Carrillo, B. (2020). Early rainfall shocks and later-life outcomes: Evidence from Colombia. _The World Bank Economic Review_ , 34(1), 179-209.

Chaudhuri, S., Jalan, J., and Suryahadi, A. (2002). Assessing household vulnerability to poverty from cross-sectional data: A methodology and estimates from Indonesia. _Discussion Paper_ #:0102-52, Department of Economics, Columbia University, New York.

Chaudhuri, S. (2003). _Assessing vulnerability to poverty: concepts, empirical methods, and illustrative examples_ . Mimeo. Department of Economics, Columbia University, New York, 56.

CCKP (2023). Climate Change Knowledge Portal Temperature Country Data. Retrieved by January 30, 2023, from https://climateknowledgeportal.worldbank.org/country/ecuador/trends-variabilityhistorical.

CEPAL (2010). _Panorama Social de América Latina 2010._ Santiago de Chile: Comisión Económica para América Latina y el Caribe, CEPAL. https://www.cepal.org/es/publicaciones/1236-panoramasocial-america-latina-2010.

Damania R., Desbureaux, S., Hyland, M., Islam, A., Moore, S., Rodella, A.S., Russ, J., Zaveri, E. (2017). When Rainfall Is Destiny: The Long-Lasting Impacts of Water Shocks on Families. In: _Uncharted Waters: The New Economics of Water Scarcity and Variability._ 35-46. The World Bank. <u>https://doi.org/10.1596/978-1-4648-1179-1_ch3.</u> Drumond, A., Stojanovic, M., Nieto, R., Gimeno, L., Liberato, M. L., Pauliquevis, T., ... & Ambrizzi, T. (2021). Dry and wet climate periods over Eastern South America: identification and characterization through the SPEI index. _Atmosphere_ , 12(2), 155. <u>https://doi.org/10.3390/atmos12020155</u> Guha-Sapir D., R. Below, & Ph. Hoyois (2022). _EM-DAT: The CRED/OFDA International Disaster Database_ – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium.

IPCC, 2007: Climate Change 2007. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. Van Der Linde, and C.E. Hanson (eds.)]. Cambridge University Press, Cambridge, UK, pp. 7-22.

Skoufias, E. & Baez, J. (2023). Operationalizing Vulnerability to Poverty. _Working paper_ . The World Bank Skoufias, E., Vinha, K., & Beyene, B. M. (2023). Quantifying vulnerability to poverty in the droughtprone lowlands of Ethiopia. _Journal of African Economies_ , ejad003. <u>https://doi.org/10.1093/jae/ejad003</u> Socioeconomic Database for Latin America and the Caribbean (2022). <u>https://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/. CEDLAS and The World Bank.</u> Retrieved September 2023.

ThinkHazard! Platform (2022). https://thinkhazard.org/. Global Facility for Disaster Reduction and Recovery (GFDRR Labs). The World Bank. Retrieved March 2022.

UNDHA (1992). _Internationally agreed glossary of basic terms relating to disaster management_ . United Nations Department of Humanitarian Affairs, Geneva, Switzerland.

31
# **_Estimation issues_**

The first estimation issue is potential endogeneity in the export exposure covariate. Since we observe changes in labor outcomes and exports simultaneously, we cannot identify which is driving the other. To ensure exogeneity of our export exposure, we need a variable that predicts exports from Cambodia based solely on its trading partners internal demand growth, rather than supply-side determinants. Hence, we construct our instrument using time series regressions of Cambodia’s exports to the United States, its largest trading partner, on U.S. GDP by industry at the four-digit level, as we explained in the next few paragraphs.

The choice of the instrument is grounded in two reasons. First, Cambodia has a highly concentrated export basket both in terms of merchandise (garments) and trading partners (the United States).<sup>2</sup> This characteristic makes other country imports a less suitable proxy for Cambodia's exports external demand. Therefore, our model best suits the Cambodian exporting context.

0 1 , (3) In equation (3), are predicted values from time-series regressions of Cambodia’s exports to the United States on the U.S. GDP by industry, as a proxy for Cambodia exports dd tt,tt+nn to the United States solely explained by external aggregate demand. zz A second estimation issue is the validity of segmented labor market assumption. Existing labor mobility barriers or rigidities (such as commuting costs or lack of transport infrastructure) allow us to observe variations in local labor market outcomes and, as a result, to estimate the effects of differentiated exposure to trade.

Qualitative evidence in the literature suggest that labor markets are geographically segmented. For example, an Asian Development Bank (ADB) report highlights that recruiting from Phnom Penh and some other major cities is reportedly very difficult. On the other hand, firms do not appear to be offering wages above the legal minimum (plus legally required bonuses) to attract workers. Further, one heuristic method for assessing labor market integration involves examining the standard deviation of wages across regions and over time. This heuristic measure is used because various factors can prevent wage equalization across regions. To investigate the level of labor-market integration in Cambodia, we calculate district premiums, which can indicate the presence of segmented labor markets. Table A1 in the Annex clearly shows that wages are not equal across districts in Cambodia, providing strong support for the presence of segmented labor markets during our study period.

> 2 Since Cambodia became the first least-developed country to join the World Trade Organization (WTO) in 2004, trade has steadily increased.  The United States is Cambodia’s largest single-country export destination, with approximately 40 percent of Cambodia’s total exports going to the United States – primarily comprised of garment, footwear, and travel goods products (International Trade Association, United States).

23 | P a g e