rafmacalaba/datause-encoder-data huggingface dataset:
with unobserved differences in child ability. Second, we know whether the decision to enroll in high school was made, but being off by a year will imply that for some observations the predicted increase in migrant share from the village will not be perfectly mapped into the year of the decision. The quartic in years-since-IDs is a smooth function, and we do not observe extremely sharp jumps in the predicted migrant share variable from one year to the next, and so we believe it unlikely to be systematically related to within village variation in unobserved ability.

While we may not worry about small errors in assumed date of school entry, or lack of a pretreatment trend for ID distribution, these observations do not rule out the possibility that ID distribution was driven by increases in the number of local residents wanting to migrate, which may have arisen either in the same year or in the year or two preceding ID distribution. As earlier work finds that large prior year rainfall shocks are associated with out-migration (Giles and Yoo 2007), we address this possibility by estimating hazard models for ID distribution. In these models, we examine the possible links between ID distribution and exogenous rainfall shocks, which affect local agricultural productivity and therefore the returns to labor in both agricultural and non-agricultural sectors. When large negative shocks occur, incentives to migrate increase and families may pressure village or county leaders to distribute IDs.

We construct our preferred measure of “significant rainfall shocks” from thirty years of monthly rainfall data collected from county weather stations in each of the 52 counties where RCRE villages are located. The shock we use is the square of the deviation of July-November rainfall from the thirty year average rainfall over these months. The dependent variable in the estimated hazard models takes a zero before IDs are issued and one in the year of issue, after which the village drops from the dataset. The indicator for ID distribution is then regressed on province-year dummy variables and the squared rainfall shocks. In successive models shown in Table 4, we use the one year lag in the squared rainfall shock, the two year lag, and the combination of the two lags to assess whether negative shocks to the local economy might affect the demand for IDs and influence the timing of their distribution. Not only do we find no significant relationship between rainfall shocks and the timing of ID distribution, but point estimates of the marginal effects are also quite small. The combination of these factors lend confidence that incentives to migrate did not drive the timing of ID distribution.

The timing of ID distribution may not be correlated with trends in educational attainment or 15
# 5.  Results

## 5.1.   Food insecurity

We estimate the relationship between conflict and food insecurity using the round-based panel because the CSI data have been collected in each wave and visit to measure seasonal changes in food insecurity. Table 2 shows the results from estimating the effect of victimization on the standardized CSI and the (standardized) components used in constructing the index. An additional conflict event is associated with the increase in the CSI by around 0.044 to 0.052 standard deviations (table 2, columns 1–3). Results are robust to adding controls (geographical and household specific, as listed in table C.1) and regionsurvey round fixed effects that capture region-specific time trends (table 2, column 3).

After a conflict event, a household might receive assistance from informal safety nets to overcome the event’s consequences. In order to understand how receiving assistance might affect our findings, in column 4 of table 2 we removed households that reported having received any assistance after the most recent event (from the telephone survey) as well as households that reported having received either cash or in-kind transfers in the GHS panel. A small subset (only 6 percent) of the households reported any assistance. Removing these households from the sample brings down the coefficient estimate to 0.038 standard deviations, but the result remains significant at the 5 percent level. This suggests that households that received any form of assistance were perhaps slightly more affected by the adverse events than households that did not receive assistance.

Columns 5–14 in table 2 show that the household coping strategies used after a conflict event are most often the kind that reduce food consumption: limiting portion sizes, restricting adult consumption so children can eat, and reducing the number of meals. Interestingly, households do not increase their borrowing of food or their reliance on help, results that are in line with the low level of assistance reported after a conflict event.

In table 3 we analyze whether the effects of victimization on food insecurity vary by event type and across perpetrators. Property-related events include robbery of dwellings and individuals, having one’s dwelling destroyed or occupied, having one’s land occupied or destroyed, and having household assets destroyed. Violent events include killings, injuries, and physical aggression.<sup>23</sup> Furthermore, we run the analysis by the perpetrator of the event.

> 23 The rest of the event categories include sexual violence and a household member being forced to work, being kidnapped/abducted, being made a refugee or internally displaced, and being restricted from going to school or

17
1S Table 13 on PPWW per worker (accrued, not necessarily paid) for the years 1990 and 1991 and the first half of 1992 show that AAA firms have been paying the<sup>highest excess wage taxes,</sup> demonstrating the link between higher profits and wages. In 1992 in<sup>particular AAA firms are</sup> accruing much more PPWW, and also paying it (recall Table 9 on tax<sup>arrears).</sup> Table 13: PPWW per Worker (Thousands of Zlotys)

||<br>J1990|<br>1991|<br>1992<br>(1.6)|
|---|---|---|---|
|AAA|_3655_|6500|1635|
|AA|5675|4740|219|
|A|1319|1518|256|

In assessing wage restraint, however, a useful measure is provided in Figure 8, which plots the share of wage costs in a crude measure of gross value added (pre-tax profit<sup>plus depreciation plus</sup> wage costs = GVA). The starting point for the graph is arfificially low because of the stock profits of late 1989 and early 1990. Further, there was a huge compression of real wages<sup>in first quarter</sup> 19°50 to offset the shock of higher costs and interest rates. The collapse<sup>of GVA underlies the sharply</sup> rising share of wage costs in OVA for A firms, virtually consuming all the GVA<sup>by 1992. For</sup><sup>_AAA_</sup> firms, this share rises, but does so at a diminishing rate. These results<sup>show that the higher wages in</sup> AAA _fms_ are accompanied by a maintenance of surplus generation.

<!-- Start of picture text --> Wages/Gross Valu- Added AAA Wages/Gross Value Added AA Wages/Gros Value Added A<br>(r. GU KAO-1 (s.w.4 . _1)<br>Ln~~~~~~~~~~~~~~~~3<br>ILI' u r - ;U' - few - -<br>0.11'U A<br>0.14- GAt .. '<br>.1"1 *I<br>LI- Ie Mt<br><!-- End of picture text -->

# Figure 8

Table 14 contains two measures of decapitalization. The first is the ratio<sup>of accrued PPWW</sup> to disposable cash, which represents the constraint on paying PPWW<sup>after income tax and the</sup> dividend, and adding back depreciation, which is a non-cash expense.<sup>The second captures the claims</sup> on pre-tax profit, namely, income tax, dividend and PPWW. If the<sup>sum of these claims exceeds pre-</sup> tax profit, then the excess must cut into the depreciation allowance or be<sup>funded by increased</sup> borrowing. According to the first measure, AAA firms and AA firms<sup>have roughly the same results;</sup> but A firms are clearly in a state of decapitalization. The time path of the second<sup>measure is</sup> interesting. All three groups were more-or-less level at the end of 1990; but both<sup>AA and A firms</sup> indicate decapitalization thereafter. (A firns as a group had pre-tax losses<sup>in 1991 and 1992.)</sup>
**<u>Table 8: Estimated Impact of Different Types of Micro-watershed Treatment on Stream Flows and Sediment Load</u>**

|<br>**SWC on cultivated land**|**SWC on degraded land**|**New area planted with trees**|
|---|---|---|
|**Dependent variable: water flow (m**<sup>**3**</sup>**/s)**|||
|Lag treatment indicator<br>-0.35022***|-1.03252***|-1.26691***|
|(-10.81)|(-8.13)|(-5.18)|
|Daily rainfall<br>0.01313***|0.01312***|0.01312***|
|(16.46)|(16.40)|(16.37)|
|One-day lag daily rainfall<br>0.00219**<br>(2.71)|0.00219**<br>(2.69)|0.00219**<br>(2.69)|
|Two-day lag daily rainfall<br>0.00136|0.00136|0.00136|
|(1.68)|(1.67)|(1.67)|
|Three-day lag daily|||
|rainfall<br>0.00064|0.00063|0.00064|
|(0.80)|(0.79)|(0.79)|
|R<sup>2</sup><br>0.065|0.061|0.057|
|No. of observations<br>10,204|10,204|10,204|
|Elasticity<sup>a</sup><br>-0.82360***|-0.44430***|-0.16006***|
|(-10.81)|(-8.13)|(-5.18)|
|**Dependent variable: sediment load (t/d)**|||
|Lag treatment indicator<br>-3.99966***|-4.57322***|-6.44764**|
|(-7.28)|(-4.92)|(-3.15)|
|Daily rainfall<br>0.12495***|0.12476***|0.12462***|
|(21.11)|(21.04)|(21.00)|
|One-day lag daily rainfall<br>0.00622|0.00603|0.0059|
|(1.04)|(1.00)|(0.98)|
|Two-day lag daily rainfall<br>-0.00713|-0.00732|-0.00745|
|(-1.19)|(-1.22)|(-1.24)|
|Three-day lag daily|||
|rainfall<br>-0.01129|-0.0115|-0.01164*|
|(-1.91)|(-1.94)|(-1.96)|
|R<sup>2</sup><br>0.087|0.084|0.082|
|No. of observations<br>8,472|8,472|8,472|
|Elasticity<sup>a</sup><br>-1.57725***|-0.32999***|-0.13660**|
|(-7.28)|(-4.92)|(-3.15)|

_Notes:_ Year and micro-watershed fixed effects are included throughout but not reported. t-statistics are in parentheses: *** significant at 1%; ** significant at 5%; * significant at 10%. aTo facilitate comparison of treatment effects, figures presented are elasticities computed as: ��,� �������������⁄ ⁄ ����<sup>�</sup> ⁄�<sup>�</sup> � where �<sup>�</sup> and �<sup>�</sup> are mean values of outcome and treatment indicators for pixels in the TBIWRD treatment micro-watersheds.

23
Lack of exposure to international competition—partly because of non-tariff barriers and complex trade rules—as well as restrictive product market and services regulations, remain structural bottlenecks to domestic and foreign investment in the region (Shepotylo and Vakhitov 2015; World Bank 2016a). Low innovation rates—which partly stem from weak competition, inadequate control of corruption, and the dominance of state-owned enterprises—continue to dampen the business environment and hinder investment in the region, particularly in the absence of progress with other reforms (figure 9.F; EBRD 2018, 2019).

Structural reforms that help to close investment gaps and promote FDI inflows and greater participation in global value chains, by boosting private sector development and transition to competitive and inclusive markets, could help boost productivity in the region, particularly in the economies outside the EU (EBRD 2014; Gould 2018; EBRD 2018; World Bank 2019b). Greater economic integration and regional coordination could also help spur innovation and competition, and help unleash the region’s growth potential (Kunzel et al. 2019). The pace of future growth will largely depend on the successful implementation of structural reforms to improve the business environment, achieve debt sustainability, and restructure state-owned enterprises (Belarus, Kyrgyz Republic, Moldova, Ukraine, Uzbekistan; EBRD 2017; Funk, Isakova, and Ivanyna 2017).

Measures to improve the climate for private investment need to be complemented by improvements in public investment, including by better prioritizing public expenditures and enhancing the appraisal and review of public investment projects. Even in ECA’s EU member states, public investment efficiency can be as much as 2 percent of GDP lower than in other EU countries. Sound policies with respect to infrastructure investment and improvements in governance, education, and public health might help countries become more integrated into global and regional value chains.

# 7. Latin America and the Caribbean

# 7.1 Introduction

Latin America and the Caribbean (LAC) accounted for around 13 percent of emerging market and developing economy (EMDE) investment during 2000-21. Investment growth over the period was volatile. Following subdued growth in the early 2000s, there was a surge in investment in the period up to 2011 (temporarily interrupted in 2009 by the global fi nancial crisis), followed by a long, fallow period from 2012 to 2020 when annual investment growth was never above 3.5 percent, and negative in fi ve years.

Throughout the period, there was close co-movement between investment growth and commodity price changes, the major driver of terms of trade changes in LAC. Indeed, the marked decline in investment growth from 2010-16 was concentrated in South American commodity exporters such as Brazil, Chile, and Peru, while investment in Central America and the Caribbean was more resilient. Global fi nancial conditions, and U.S. monetary policy in particular, are also important determinants of investment cycles in LAC. Following a strong rebound from the pandemic trough of 2020, investment is forecast to
# **6.4 Falsification test for pre-crisis period**

Might our results be a quirk of the 2006–09 period that we consider? In this subsection we introduce a placebo test designed to investigate the possibility that banks with extensive crisis experience were more risk-taking and may thus have lent significantly more during the pre-crisis period. The implication of this assertion is that excess pre-crisis lending might force such banks to cut their post-crisis loan volumes more drastically. If this assertion is indeed true, we would expect a positive and significant sign of the coefficient on crisis experience in the period preceding the crisis.

Table 5: Falsification test regressions for the difference in bank lending between 2003 and 2006, decomposed crisis experience<sup>_†_</sup>

||**E1**|**E2**|**E3**|**E4**|**E5**|**E6**|
|---|---|---|---|---|---|---|
|Crisis experience|-0.052|-0.058|-0.068|-0.031|-0.047|-0.004|
||(0.03)<sup>_∗_</sup>|(0.03)<sup>_∗_</sup>|(0.05)|(0.05)|(0.06)|(0.09)|
|Size||0.000|0.000||0.000|0.000|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Solvency||-0.214|-0.232||-0.212|-0.226|
|||(0.11)<sup>_∗∗_</sup>|(0.11)<sup>_∗∗_</sup>||(0.11)<sup>_∗_</sup>|(0.10)<sup>_∗∗_</sup>|
|Proftability||0.015|0.014||0.015|0.013|
|||(0.02)|(0.01)||(0.02)|(0.01)|
|Interest margin||0.264|0.231||0.256|0.221|
|||(0.31)|(0.33)||(0.31)|(0.32)|
|GDP per cap.|||-0.000|||-0.000|
||||(0.00)|||(0.00)|
|Lagged GDP|||-0.937|||-0.813|
|growth|||(1.03)|||(1.05)|
|Current account|||-0.222|||-0.038|
|balance|||(0.25)|||(0.30)|
|Infation|||-0.188|||-0.252|
||||(0.56)|||(0.57)|
|Host FE|Yes|Yes|Yes|Yes|Yes|Yes|
|1st-stage _F_||||0.00|0.00|0.00|
|Underid. test||||0.00|0.00|0.00|
|Weak id. test (rel. bias)||||_<_ 5 %|_<_ 5 %|_<_ 10 %|
|Overid. test||||0.12|0.05|0.05|
|_R_<sup>2</sup>|0.01|0.03|0.03||||
|2nd-stage _F_|0.09|0.04|0.00|0.54|0.09|0.00|
|Estimation|OLS|OLS|OLS|IV|IV|IV|
|Obs. (host countries)|288 (75)|287 (74)|286 (74)|262 (52)|262 (52)|261 (52)|

> _†_ The dependent variable is the change in bank loan-to-asset ratio between 2003 and 2006. Heteroskedasticity and host country correlation-robust standard errors are reported in parentheses. A constant term were included in the regressions, but not reported. Underid. test: KleibergenPaap rk LM statistic, weak id. test: Kleibergen-Paap rk Wald F. statistic and overid. test: Hansen J. statistic. _∗_ indicates significance at 10 percent level, _∗∗_ indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

Our test involves altering the pre- and post-crisis dates to an earlier period; we choose 2003 and 2006 as our alternative years. In addition to being a test of whether experienced banks lent more in the pre-crisis period, this test can also determine whether bank lending behavior in response to 27
|**Table 3: Firm Characteristics and Performance Summary Statistics**<br>**Variable**|**Obs**|**Mean**|**SD**|**p10**|**p90**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|
|**_a. Firm characteristics (2019)_**||||||||
|Log(labor productivity)|3,647|10.59|1.20|8.98|12.01|3.95|16.45|
|Log(number of employees)|3,647|2.62|0.97|1.61|3.93|0.00|7.16|
|Log(firm age)|3,647|2.88|0.55|2.08|3.40|0.69|5.03|
|Top manager female|3,647|0.20||||||
|Innovated during 2017-19|3,647|0.43||||||
|State ownership (10%)|3,647|0.00||||||
|Foreign ownership (10%)|3,647|0.07||||||
|Has line of credit or loan|3,647|0.42||||||
|Owns a website|3,647|0.65||||||
|_Main market_||||||||
|Local|3,647|0.41||||||
|National|3,647|0.48||||||
|International|3,647|0.12||||||
|_Sector_||||||||
|Manufacturing|3,647|0.27||||||
|Retail|3,647|0.20||||||
|Other services|3,647|0.53||||||
|**_b. Firm performance (Round 3)_**||||||||
|Percentage change in monthly sales relative to one year earlier|3,507|-1.99|33.91|-40.00|25.00|-100.00|400.00|
|Percentage change in number of permanent full-time workers since December 2019|3,546|-14.60|51.38|-80.00|28.57|-200.00|200.00|
|Reduced number of permanent full-time workers since December 2019|3,546|46.96||||||
|Anticipate fallinginto arrears on outstandingliabilities in the next 6 months|3,247|20.85||||||

_Sources_ : Enterprise Surveys and Enterprise Surveys COVID-19 Follow-up Surveys.

_Note_ : In our sample, the percentage of firms who have state ownership (10%) is 0.17 percent.

15
WPS3885

# **BUSINESS ENVIRONMENT AND LABOR MARKET OUTCOMES IN EUROPE AND CENTRAL ASIA COUNTRIES**<sup>**1**</sup>

# **Paloma Lopez-Garcia**<sup>**_2_**</sup>

## World Bank Policy Research Working Paper 3885, April 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This paper was prepared as a background paper for the ECA regional study Enhancing Job Opportunities: Eastern Europe and the Former Soviet Union

> 2 Bank of Spain. Research Department, Alcala 48, 28014 Madrid. Correspondence to paloma.lopezgarcia@bde.es

1
A recent paper by Berlinski and Galiani (2005) analyzes the impact of a large program to construct pre-school facilities in Argentina in the 1990s on pre-primary school attendance and maternal labor supply. The intensity of the program varied by region, and Berlinski and Galiani use a differences in differences framework to identify program impact. The estimation also includes cohort and region dummies to control for nationwide trends and level differences across regions. The identifying assumption is therefore that regions that received more preschool facilities would not have had different growth rates in enrollment in the absence of the construction program. (Berlinski and Galiani provide evidence that this is a reasonable assumption.) Berlinski and Galiani conclude that the program had a large, positive impact on preschool enrollment—indeed, they cannot reject the null hypothesis that all new preprimary school slots were taken up by children who would otherwise not have been in school. The authors take this as strong evidence for a supply constraint on preschool enrollment in Argentina.

A follow-up paper by Berlinski et al. (2005) analyzes the impact of the preschool construction program on test performance in Spanish and mathematics in third grade. The analysis provides compelling evidence of significant program impacts. Intent-to-treat estimates show that children in cohorts and regions exposed to the construction program have significantly higher test scores. Preschool participants also appear to perform better on noncognitive dimensions of development, including paying attention in class, being disciplined, and being participative, as reported by their third-grade teachers. Convincingly, Berlinski et al. provide evidence from a “placebo experiment” which shows that children in these same schools in other cohorts that were not affected by the preschool construction program do not appear to have higher test performance—an important check on their identification strategy.

Various papers by Grantham-McGregor and her co-authors use data from Jamaica to analyze the short- and medium-term impact of interventions in early childhood. In one study, a lottery was used to divide a sample of stunted children ages 9-24 months into four groups: The first study group received a food supplement of 1 kg. of milk-based formula provided weekly; a second study group received early childhood stimulation, specifically weekly home visits by social workers who demonstrated play with home-made toys and discussed parenting issues with mothers; the third study group received both the supplement and the stimulation; the fourth group served as a control group. In addition, data was collected on a sample of non-stunted children. Children in all of the study groups were then followed over time. Results after two years suggested that both the stimulation and nutritional supplement interventions had positive impacts on child development as measured by the Griffiths Mental Development Scales. The largest effects were found in the group that had received both interventions—after two years these 18
**Table 2: Estimated Impact on Nominal Imports (% change of baseline)**

||_Case 1(_|_2006)_|_Case 2(_|_2006)_|_HSW 2004_|
|---|---|---|---|---|---|
|importers|_Expor_|_ters_|_Expor_|_ters_|_Exporters_<br>|
||_APEC_|_World_|_APEC_|_World_|_APEC_|
|_1 AUS_|0.0|-0.5|-0.7|-0.5|0.0|
|_2 CAN_|-2.2|-2.4|-2.1|-2.1|0.0|
|_3 CHL_|3.1|0.0|1.6|0.4|0.0|
|_4 CHN_|30.4|30.2|27.1|26.7|29.0|
|_5 HKG_|3.1|2.0|1.0|0.3|0.0|
|_6 IDN_|15.6|16.9|11.9|12.3|20.3|
|_7 JPN_|3.2|1.1|2.1|1.2|0.0|
|_8 KOR_|20.9|17.8|20.1|18.2|0.4|
|_9 MEX_|20.2|19.7|19.0|17.7|17.7|
|_10 MYS_|10.3|11.0|8.4|9.0|12.1|
|_11 NZL_|-0.7|-0.6|-0.9|-0.4|0.0|
|_12 PER_|31.2|30.5|24.4|22.3|31.0|
|_13 PHL_|60.0|62.9|55.1|57.1|47.6|
|_14 RUS_|70.6|69.3|37.8|39.2|100.7|
|_15 SGP_|2.0|1.6|2.0|2.1|0.0|
|_16 THA_|132.7|117.6|128.2|124.3|36.7|
|_17 CTP_|2.3|1.4|1.6|1.5|_n.a._|
|_18 USA_|1.3|-0.9|0.2|-1.1|0.0|
|_19 VNM_|37.0|44.1|27.3|31.2|73.6|
|_20 EU27_|_n.a._|-1.6|_n.a._|-1.5|_n.a._|
|_21 ROW_|_n.a._|-0.8|_n.a._|-1.0|_n.a._|
|_APEC Total_|12.9|11.7|11.0|10.0|_n.a._|
|_World_|11.7|4.5|7.2|3.6|_n.a._|

(Note) Authors’ calculation using the GTAP model, GTAP database version 7 pre-release 6, and Gempack. Row denotes importers, and Columns denotes exporters.

In general, the reforming economies in APEC tend to increase imports. The impact is larger in the economies that undertake more significant reforms, in particular Russia, Thailand, Vietnam and the Philippines. In contrast, APEC member economies that have achieved a higher level of transparency experience minimal increases in imports<sup>17</sup> . The results based on our CGE model simulation here are generally comparable with those by HSW. This is to be expected because both studies use the same estimates for reductions in trade costs. The relatively minor differences in results likely arise from the difference between assumed elasticities of substitution of imports in the GTAP model, i.e.

17 The large increase in the imports of Korea reflects the assumed reduction of irregular payment.

21