rafmacalaba/datause-encoder-data huggingface dataset:
rate paid for time deposits, plus a markup for the estimated cost of mobilizing and servicing such deposits, as well as the cost of holding the related reserve requirement.

For Banadesa, the social opportunity cost of public debt was estimated at about 14 percent nominal, or 6 percent real, in 2002, and at 12 percent nominal, or 5 percent real, in 2003 (percentages are based on data provided by the Banco Central de Honduras).

**_Calculating the subsidies and the delivery cost of Banadesa’s products._** The SDI is calculated for 2002, the latest year for which data for both Fondos Propios and Fideicomiso were available.<sup>19</sup> As shown in table 1, the SDI was calculated for (a) Banadesa’s Fondos Propios, on the basis of financial statements audited by external auditors, and (b) consolidation of those audited statements reports for Fondos Propios and Banadesa’s unaudited, self-reported Fideicomiso statements.<sup>20</sup> Table 1 also presents the SDI that results from incorporating an (arbitrary) increase in provisions for loan losses of 7 percent,<sup>21</sup> a figure that accounts for the fact that loan losses provisions are underestimated (this adjustment serves in computing the lower limit of the SDI). Obviously, the real value of such provisioning requires a thorough audit work, which is out of the scope of this study. These results are also reported for both Fondos Propios and Consolidated.

**Table 1. SDI Calculation (2002)**

(Figures in Millions of Lempiras, Unless Otherwise Noted)

|||**_Fondos_**<br>**_propios_**|**_Fondos_**<br>**_propios_**<sup>_1_</sup>|**Consolidated**<br>(incl._fondos_<br>_propios_and<br>_fideicomisos_)|**_Consolidated_**<sup>_1_</sup><br>**_(_**_incl._ _fondos_<br>_propios_ _and_<br>_fideicomisos_**_)_**|
|---|---|---|---|---|---|
|LP|Average loan portfolio|153|153|378|378|
|LP*I|Revenue from loans|70|70|99|99|
|I|Yield on lending|46%|46%|26%|26%|
|P|Profits (losses)|(18)|(26)|3|(5)|
|E|Average equity|(111)|(111)|209|209|
|M|Social opportunity cost|14 %|14%|14%|14%|
||Average debt to|54|54|74|74|
|A|government (IDB-linked)|||||
||Interest expenses on debt|1.5|1.5|2.5|2.5|
|A*C|to government|||||
||Rate paid for debt to|3%|3%|3%|3%|
|C|government|||||
|A*(M|Discount on debt to|6|6|8|8|
|-C)|government|||||
|E*M|Subsidy on equity|(16)|(16)|29|29|

> 19 The calculation of SDI takes into account the data limitations noted, including Banadesa’s deficient financial statements; the existence of three different sets of accounts (from Banadesa, the external auditor, and CNBS); and the unavailability, at this stage, of audited financial statements for 2003.

> 20 Seeking to obtain the most realistic consolidated accounts possible, the consolidation added additional accounts to the audited Fondos Propios statements (which are likely “contaminated” because they do not distinguish between sources of funding for Fondos Propios or Fideicomiso).

> 21 Table 1 illustrates how an adjustment in loss provisions (however small) would be reflected in the SDI.

16
12 us to better capture the change in labor productivity due to structural reforms. Second, although the TFP, the most used measure of productivity, accounts for the level of capital and the production technology, we are unable to capture the change in TFP stemming from structural reforms as the WBES database collects information only on the current stock of capital. The information on the past stock of capital required for calculating retrospective TFP is not reported. Recall that the standardized WBES data set used in this study has a pseudo panel structure.<sup>12</sup> The sensitivity analysis explores the robustness of the findings using TFP and value-added per worker as alternative measures of productivity.

Labor productivity growth is computed as the annual average growth of labor productivity over the last three years. Firms are asked during the surveys to report their total annual sales and full-time employees at the end of the previous fiscal year (�−1) and three years ago (�−3), respectively. At each period, labor productivity �� is computed as the ratio of total annual sales over total permanent full-time employees. Following Davis and Haltiwanger (1999) and World Bank (2016), we compute labor productivity growth by dividing the change in productivity between �−1 and �−3 ������ – ������ by the average value of initial and final <u>������ � ������</u> labor productivity . This approach helps to reduce the influence of outliers. � Further, since there were two full years between the two points in time, we calculate the annual average labor productivity growth as follows: � <u>������ – ������</u> ����� � � ∗ <u>������ � ������</u> (1) � ����� is ranged between -1 and 1 and refers to the information on labor productivity growth available at time t.

To construct the structural reform indexes, we use the SB indicators in the MONA database as described above. The IMF supporting programs begin during the approval year and are supposed to finish at the initial end period. Only a very few programs end early or are delayed until the next year. In general, the IMF supporting programs cover a 3-year period. We focus on the SBs that have been met or met with minor delay during the last review of the program

> 12 The lack of panel data on firms limits the possibility to use robust measures of productivity such as the TFP from Levinsohn-Petrin (Levinsohn and Petrin, 2003).
in regression I. In regression II, land size has an even higher coefficient and is marginally significant. Height, on the other hand, has significant effects in regressions III and IV only on the error term, with taller people having a lower error propensity, but not on utility curvature. BMI has no effect. The verdict is thus mixed. While the structural modeling improves our correlations somewhat relative to the non-parametric measures, several effects that were significant with the 2-parameter model are not picked up. This is likely due to the bad fit of the 1- parameter model. Indeed, the complex patterns of risk seeking combined with risk aversion visible from the nonparametric data points cannot be accommodated by such a function. This, in turn, means that error rates will be larger. And indeed we can see that the standard errors in the expected utility model are considerably larger than the ones seen in the dual model presented above.

**Table 5:** Stability analysis EU

||I||I|I|I|II|I|V|
|---|---|---|---|---|---|---|---|---|
||_ρ_|_σ_|_ρ_|_σ_|_ρ_|_σ_|_ρ_|_σ_|
|land size|0.186***<br>(0.066)|0.007<br>(0.009)|0.293*<br>(0.156)|0.032*<br>(0.019)|||||
|distance road|0.054|0.003|0.119|0.004|||||
||(0.081)|(0.009)|(0.132)|(0.014)|||||
|altitude|-0.280***<br>(0.076)|-0.036***<br>(0.007)|-0.134<br>(0.137)|-0.019<br>(0.018)|||||
|height|||||0.042<br>(0.066)|-0.023***<br>(0.004)|0.045<br>(0.070)|-0.030***<br>(0.005)|
|BMI|||||||0.005|-0.005|
||||||||(0.025)|(0.003)|
|literate|-0.159|0.002|-0.216|0.025|-0.187|-0.008|-0.203|-0.007|
||(0.104)|(0.015)|(0.150)|(0.020)|(0.127)|(0.015)|(0.127)|(0.016)|
|middle school|-0.250|-0.005|-0.389|-0.029|-0.386|-0.010|-0.417*|-0.008|
||(0.222)|(0.020)|(0.244)|(0.026)|(0.238)|(0.021)|(0.237)|(0.021)|
|business|-0.366|-0.030|-0.429|0.021|-0.430|-0.058|-0.363|-0.049|
||(0.309)|(0.054)|(0.446)|(0.063)|(0.359)|(0.057)|(0.424)|(0.064)|
|female|-0.329*|0.040|-0.293|0.001|-0.500*|0.002|-0.479*|-0.003|
||(0.188)|(0.029)|(0.254)|(0.029)|(0.262)|(0.032)|(0.277)|(0.033)|
|age|-0.179***|-0.012*|-0.290***|-0.017|-0.134**|-0.005|-0.135**|-0.006|
||(0.048)|(0.007)|(0.074)|(0.012)|(0.060)|(0.007)|(0.059)|(0.007)|
|unmarried|0.446**|-0.003|1.027**|0.043|0.606**|0.025|0.613**|0.030|
||(0.185)|(0.024)|(0.450)|(0.045)|(0.290)|(0.030)|(0.306)|(0.031)|
|region fxed efects|✓|✓|✓|✓|✓|✓|✓|✓|
|constant|1.830***<br>(0.117)|0.193***<br>(0.015)|1.915***<br>(0.252)|0.143***<br>(0.022)|1.578***<br>(0.118)|0.177***<br>(0.017)|1.482***<br>(0.506)|0.279***<br>(0.070)|
|N_clust|493||254||496||496||
|chi2|45.85||49.57||16.40||16.40||

Standard errors in parentheses; *p<0.1, **p<0.05, ***p<0.01 Continuous independent variables are entered as z-scores 23
_25_

# 4. DISTRIBUTIONAL OUTCOMES OF ADJUSTMENT: A SYNTHESIS

What were the distributional consequences of adjustment in the six countries in our sample? - We have already seen that, while the countries shared common targets during adjustment closing the expenditure-income gap that had developed before and during the crisis - initial conditions and characteristics were different. There are many ways in which we could compare results for our sample countries. We could compare the experience of Indonesia and Malaysia, the only two countries that adjusted without recourse to IMF and World Bank stabilization and structural adjustment loans. Or we could synthesize the distributional experience of our sample by examining successively how each country performed in terms of distributional indicators: wages, primary incomes, poverty, and income distribution. What we have chosen to do, however, is to rely on three pairwise comparisons: Chile and Malaysia, classified as manufacturing exporters because of the relatively larger share of their industrial sectors and the role of manufactured exports and the two most open economies at the time of the crisis; C6te d'lvoire and Morocco, primary product exporters already on the edge of crisis even before the debt crisis (Morocco had several failed stabilization experiences before embarking on adjustment programs); and Ecuador and Indonesia, the two oil exporters.

Chile and Malaysia. The two economies are clearly the most outward oriented in the sample and are certainly among the developing countries with the least government intervention in goods and factor markets. They experienced the least prolonged shocks, and much of their need to adjust was self-generated. As described by Meller and by Demery and Demery in their country studies, both countries followed policies ihat led to a sharp real appreciation of the exchange rate. In while, the appreciation occurred because the government used the exchange rate to bring down inflation while the capital market was open. Inflows of short-term private capital fueled a private expenditure surge
# **1. INTRODUCTION**

Analysis of the impact of trade liberalization on the household welfare generally falls under one of the two main approaches; ex-ante studies, where change in prices due to a proposed trade liberalization is simulated and then in turn is used to estimate the change in household welfare, and ex-post econometric studies, where the data before and after the change in prices are used to estimate changes in household welfare<sup>1</sup> . Some examples of the former are Ravallion (1990) and Minot & Goletti (2000). Dercon (2005), Nicita (2004), and Porto (2003) are among the few studies that employ the latter methodology<sup>2</sup> . Typically, the main impact of trade reforms on household welfare is through the changes in prices. The standard literature result<sup>3</sup> is that net sellers of the good will benefit while net buyers will suffer in response to an increase in the price<sup>4</sup> . However, as Porto (2005) shows, the net buyer approach might be misleading, as it’s only taking into account the static response. In fact, the quantity response (both from the consumer side, i.e. substitution, and producer side i.e. increasing production) as well as other indirect links are likely to play a big role. This paper investigates the importance of the various links in household’s dynamic response (hence welfare) using Vietnam data before and after major trade reforms. Although, it is hard to attribute all the changes in household welfare to trade reforms, as Litchfield et al. (2003), by constructing a counterfactual without the reforms, find the liberalization was a crucial component of the decline in poverty in Vietnam.

In general, poor households spend a majority of their income on food. The expected effect of an increase in food prices on the urban poor, who are net buyers of food, will be negative. For rural poor the results are ambiguous due to two offsetting forces. In rural areas majority of the households (including the poor) are also producers of food, hence the net effect will depend on whether the household is ultimately a net

> 1 For both of these approaches, both impact analysis and analysis allowing for behavioral responses have been used in the literature.

> 2 Reimer (2002) provides a good survey of the literature.

> 3 For instance, Mellor (1978) (examining the effect of changing relative food prices on consumers, producers, employment, and agricultural production using data from India as examples), and Barrett & Dorosh (1996) (analyzing a change in the price of rice in Madagascar) observe that higher food prices will benefit the net food sellers and will have a negative impact on net food buyers.

> 4Underlying this result is the fact that in most developing countries, (particularly in rural areas) households are both producers and consumers of some of the commodities, especially major food crops.

2
8 Figure 4: Regional decomposition of U.S. arm’s-length and intra-firm trade (A) Main destinations for U.S. exports (B) Main sources for U.S. imports <!-- Start of picture text --> Percent of U.S. exports Percent of U.S. imports<br>80<br>80 Canada Mexico Canada Mexico Japan<br>Japan Netherlands Korea Germany China<br>Germany China 60 U.K.<br>60<br>U.K.<br>40<br>40<br>20<br>20<br>0<br>0<br>Arm's length Intra-firm<br>Arm's length Intra-firm<br>(C) Share of intra-firm trade in U.S. (D) Share of intra-firm trade in U.S.<br>exports imports<br>Percent Percent<br>50 90<br>45 80<br>40 70<br>35<br>60<br>30<br>50<br>25<br>40<br>20<br>15 30<br>10 20<br>5 10<br>0 0<br>BEL NLD CAN MEX MYS SGP PHL DEU JAP IRL IRL SVK JAP CRI SGP SAU MLT HUN SWE DNK<br><!-- End of picture text --> Source: U.S. Census Bureau.

Note: Top 10 trading partners, averages for 2002-14.

A.B. Residual to 100 percent is the share of all other countries in total U.S. arm’s-length or intra-firm exports (A) or imports (B).

C.D. Residual to 100 percent is the share of arm’s-length transactions in bilateral U.S. exports (C) or imports (D) with each trading partner. BEL=Belgium, NLD=Netherlands, CAN=Canada, MEX=Mexico, MYS=Malaysia, SGP=Singapore, PHL=Philippines, DEU=Germany, JAP=Japan, IRL=Ireland, SVK=Slovak Republic, CRI=Costa Rica, SAU=Saudi Arabia, MLT=Malta, HUN=Hungary, SWE=Sweden, DNK=Denmark.
The purpose of this paper is to examine how labor markets contribute to mitigating shocks due to conflict. First, we establish how households redistribute on-farm and offfarm work in responding to conflict shocks, and how this prompts a redistribution in a household’s use of time for the household head and the spouse. Second, we study if offfarm work is directed at formal labor markets or at subsistence activities that produce lower wages. Third, we explore whether responses are heterogeneous by gender. By examining the differentiated impact vis-à-vis gender, we establish whether the welfare losses generated by responses to shock—such as a reduction in leisure time or time dedicated to children—are borne differently by men and women. Lastly, we identify whether an increasing labor supply is an effective strategy for countervailing, or at least reducing, the impact of shocks.

We use data for Colombia, a country that has experienced a long-standing conflict for over 60 years. The data collected is the baseline of a longitudinal survey of 4,800 rural households, the first household survey applied in Colombia to households living in conflict regions, as until recently, most research has concentrated on forcefully displaced populations. Forced displacement, an extreme coping strategy utilized in times of warfare, is widespread in Colombia. Today, more than 3.3 million persons have fled their hometowns in order to save their lives. Research shows that forcefully displaced persons face large asset losses, as well as a severe disruption in risk-sharing mechanisms (Ibáñez and Moya 2010). Sharp drops in income push displaced women to increase their labor participation; consequently, their contributions to households’ earnings rise significantly, yet their bargaining power remains the same and domestic violence escalates (Calderón and Ibáñez 2010). Evidence regarding coping strategies adopted by households that decide to stay in conflict zones is scarce in Colombia. This paper contributes to better understanding how households cope with conflict shocks, and how labor markets become important mechanisms for preventing further losses stemming from violent conflict.

The paper also contributes to the economic literature on the use of labor markets to hedge against the consequences of economic shocks. Empirical evidence on this issue is scarce and what data are available are restricted to small samples rich in information about the use of time, or large samples with limited labor information (Kochar 1999; Rose 2001; Cameron and Worswick 2003; Ito and Kurosaki 2009). The survey used in this paper collects detailed information about households’ use of time and participation 3
## **Appendix Table 3: Round by Round results for impact of being registered on firm profits**

||Monthly<br>Profits|Truncated<br>Profits(99th)|Log<br>Profits|
|---|---|---|---|
|_Impact in Round 2 survey_|14,792|6,967|0.550*|
|(average 15 months post treatment)|(18,077)|(11,452)|(0.324)|
|_Impact in Round 3 survey_|16,049*|13,918|0.437*|
|(average 22 months post treatment)|(9,272)|(8,733)|(0.240)|
|_Impact in Round 4 survey_|12,328|10,688|0.304|
|(average 31 months post treatment)|(10,382)|(9,399)|(0.286)|
|P-value for testingequalityof impacts across rounds|0.9264|0.9565|0.9829|

## Notes:

Registration with the D.S. instrumented with offer of the 10,000 Rs, 20,000 Rs, or 40,000 Rs registration treatments. Information only treatment group excluded from these regressions.

All regressions include controls for randomization strata and for the baseline value of the dependent variable. Robust standard errors in parentheses, clustered at the firm level.

*, **, and *** indicate significance at the 10, 5 and 1 percent significance levels respectively.

# **Appendix Figure 1: Formality against Firm Size in Different Countries**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>Sri Lanka municipal<br>60<br>Sri Lanka D.S.<br>50<br>40 Bangladesh tax<br>30 Bangladesh Municipal<br>20 Mexico SHCP (tax)<br>10<br>Mexico IMSS<br>0<br>1 2 3 4 5 6 7 8 9 10 11 to<br>15<br>Number of Workers<br>Percentage of Firms Registered<br><!-- End of picture text --> 35
million, that have the lowest poverty rates and that appear to be growing faster than the smaller statutory towns, with the share of the urban population in the big cities rising from 38 percent in 2001 to 42 percent in 2011 (Tripathi, 2013). Higher wage rates in larger cities will to some degree spill over to the towns and rural areas both through labor market adjustment and because they may also generate larger trade and remittance flows. Thus, it is theoretically ambiguous whether larger cities generate larger gains to the rural poor.

The Lanjouw and Murgai hypothesis that India may have experienced faster poverty reduction if smaller towns had grown as fast as the cities is consistent with evidence from other countries on the relationship between poverty and city size (Ferré, Ferreira and Lanjouw, 2012). However, it is difficult to test this hypothesis for India, or more generally to relate variation in growth of different types of cities to variation in rural poverty reduction. One difficulty arises because it is only once every ten years that city growth (in terms of population rather than economic output) is measured in India, using the census. A lot of the variation in rural poverty reduction occurs within a ten-year censual period and so would be missed by studies that rely on the census data to measure urban growth. Another difficulty is the absence of timely and spatially detailed (e.g. at city level) economic statistics.

This study uses a proxy for urban growth to test the hypothesis that it is the growth rates of India’s secondary towns, rather than the big cities, that matter most for rural poverty reduction. We use nighttime lights, as detected by satellite, to measure urban growth, distinguishing between growth on the extensive and intensive margins, and between the growth of cities and of secondary towns. These new measures of urban growth are econometrically related to sub-national poverty estimates that are formed at a finer spatial resolution than in the existing literature. Specifically, we use a division of India into 59 National Sample Survey (NSS) regions that are more finely-grained than the usual division into states and union territories. Our study covers four observations for each of these regions between 1993/94 and 2011/12, based on NSS “thick” rounds (with larger sample sizes such that the survey is representative at the NSS regional level). We also account for the spatial autocorrelation that is increasingly apparent in patterns of rural poverty in India.

The following section provides a simple theoretical model of a three-sector labor market in which one of the sectors—the “big city”—has a labor market distortion, but wages are flexible in the other two sectors, the secondary towns and the rural hinterland, with workers free to move between the two. For this model, we derive conditions under which technical progress in the big cities has less impact on the rural wage rate than does technical progress in the secondary towns. However, this is only one possible outcome. Even in this 3
# **Table 7: Impact of CBP and HBP programs on cognitive development** **<u>(results for children ages 2–5 years at baseline)</u>**

|||||C vs HB|P||C vs CB|P|
|---|---|---|---|---|---|---|---|---|
|||N|Impact|SE|P-value|Impact|SE|P-value|
||**Peabody Vocabulary test**||||||||
|1|TVIP: Raw score|3,717|0.02|(0.068)|0.772|0.023|(0.066)|0.727|
|2|TVIP: Age equivalent|3,677|0.01|(0.062)|0.866|0.008|(0.059)|0.895|
|3|TVIP: Age equivalent - age|3,677|0.01|(0.072)|0.89|0|(0.069)|0.995|
||**Ages and stages**||||||||
|4|ASQ communication|3,690|0.042|(0.067)|0.533|-0.041|(0.073)|0.578|
|5|ASQ gross motor|3,694|-0.066|(0.047)|0.156|-0.02|(0.046)|0.661|
|6|ASQ fine motor|3,698|0.002|(0.051)|0.977|0.073|(0.053)|0.171|
|7|ASQ problem solving|3,699|0.02|(0.057)|0.72|0.042|(0.051)|0.412|
||**Woodcock Johnson**||||||||
|8|WJ: Raw score, out of 72|3,706|0.017|(0.047)|0.719|0.031|(0.050)|0.539|
|9|WJ % answers correct|3,706|-0.007|(0.047)|0.887|0.037|(0.047)|0.435|
||**Strength and difficulties**||||||||
|10|SDQ emotion|3,707|0.065|(0.050)|0.2|0.061|(0.046)|0.187|
|11|SDQ conduct|3,707|0.035|(0.046)|0.45|-0.022|(0.049)|0.658|
|12|SDQ hyperactivity|3,707|-0.001|(0.065)|0.988|0.036|(0.062)|0.562|
|13|SDQ peer|3,707|-0.013|(0.061)|0.832|-0.003|(0.067)|0.962|
|14|SDQ pro-social|3,707|-0.052|(0.077)|0.5|-0.021|(0.085)|0.808|
||**Anthropometrics**||||||||
|15|Height for age z score|3,691|0.027|(0.038)|0.486|0.017|(0.040)|0.664|
|16|Weight for age z score|3,702|0.021|(0.026)|0.424<br>|-0.009|(0.031)|0.776|

Note: "Difference" column presents results of the regression of the dependent variable on the treatment status (either control, HBP, or CBP). Each regression is robust to the heteroskedasticity and accounts for intravillage correlation (109 clusters in total). Results are not affected by the set of control variables used. Point estimates displayed in this table are obtained controlling for sex, age dummies, sex and age dummies, and height for age at baseline.

.

19