rafmacalaba/datause-encoder-data huggingface dataset:
Finally I replicate the panel data analysis in the manufacturing sector. For this panel analysis, I follow the same methodology that was described before. Results are presented in Table 14. In this table I only show the regressions where firms’ own responses at time _t-3_ were used as the net job creation rate instead of the industry averages. The second specification restricts the sample to firms that did not export at _t-3._ Results are significant in both specifications.

# **6. Conclusion**

A large number of studies have shown that firms that export are better performers than non-exporting firms. These firms employ more workers, grow faster and they are more productive. They make a significant contribution to aggregate growth and economic development. Dollar et al. (2006) highlight the importance of the investment climate in determining the entry of firms into foreign markets. They show that in addition to technological or market driven factors, factors like finance, infrastructure, and customs service affect a firm’s decision to export. In this study I focus on another aspect of the investment climate. I analyze how rigid labor regulations, particularly EPL, can distort the exporting decision. Evidence shows that future exporters start to increase their size before they start to export. Moreover they have to be competitive in order to survive in the foreign markets. However stringent EPL increase the opportunity cost of hiring workers and lead to lower competitiveness of firms. Although labor regulations are established to protect workers and increase aggregate welfare, they have negative effects on the labor demand of firms. This discouragement in creating jobs can make firms reluctant to enter foreign markets.

In the analysis, I use a detailed firm level dataset from 26 countries in the ECA region. I look at the variation in the stringency of EPL across sectors and see how much of that explains the variation in the decision to export. The results show that sectors in which firms want to create more new jobs have lower shares of exporters. The same conclusion is drawn when the analysis is restricted to industries in the manufacturing sector. Next, I show that these findings are not sensitive to inclusion of additional firm level control variables like access to finance, past innovative performance, the skill level, and several other variables measuring the investment climate. Evidence from the panel data provides further support to the robustness of the findings.

There are only a few studies that analyze the detrimental effects of labor regulations on firm and industry performance. In a recent study, Helpman and Itskhoki (2009) construct a 18
# **Technical Appendix**

Table A.1: Summary statistics for main variables in benchmark specification, by crisis period<sup>_†_</sup>

||N|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
||||_Non-crisis_|||
|Foreign claims|2,445|17,629|52,040|3|593,879|
|Libor-OIS|2,445|11.2|4.6|6.8|22.6|
|Global banking CDS|2,445|66.0|83.4|8.9|279.4|
|Asset volatility|2,445|-0.1|0.2|-0.4|0.4|
|Loan demand<br>|2,445|0.1|0.2|-0.3|0.4|
|Infation<sup>*</sup>|2,438|7.8|22.8|-35.9|941.1|
|Growth|2,445|5.7|5.4|-37.5|63.8|
|GDP|2,445|51,824|126,587|106.8|1,148,256|
||||_Crisis_|||
|Foreign claims|990|22,263|58,476|4|377,268|
|Libor-OIS|990|88.7|49.7|42.7|211.5|
|Global banking CDS|990|128.9|64.9|33.7|229.7|
|Asset volatility|990|1.4|1.1|0.3|4.0|
|Loan demand|990|-0.3|0.2|-0.6|-0.1|
|Infation<sup>*</sup>|986|9.9|13.3|-29.4|122.9|
|Growth|990|3.1|6.3|-40.5|67.5|
|GDP|990|58,897|137,326|157.2|1,197,708|

> _†_ Summary statistics are provided for the sample between 2001Q4 and 2011Q1. The crisis period was defined as the period between 2007Q3 and 2009Q2.

> * The inflation statistics reported here suppress the outlier cases where QoQ inflation exceeds 1,000 percent.

24
We find that the program is significantly associated with the presence of fewer parasites, a lower incidence of diarrhea and anemia, and better cognitive development. Moreover, these results are extremely robust to the inclusion of all sets of covariates.

Specifically, offering the program is associated with an 18.2% reduction in the presence of parasites. The program is also associated with a 1.8 percentage-point decrease in episodes of diarrhea, which represents a 12.4% reduction in the prevalence of diarrhea with respect to the control group. It is also associated with an 8.3 percentagepoint reduction in the incidence of anemia, which translates into a 19.4% decline in the prevalence of anemia with respect to the control group. We do not find any significant difference, however, in the usual height and weight anthropometric indicators.

Finally, the program is associated with notable improvements in child cognitive development. We find that treated children have a 30.2% higher score on the McArthur test and show an improvement in the PPVT test equivalent to an increase with respect to the control group of 8.1%.

These child health results are consistent with the hypothesis that replacing dirt floors with cement floors interrupts the transmission of parasitic infestations and should therefore reduce the incidence of both diarrhea and anemia. The reduction in anemia is expected to have positive effects on cognitive development.

# **6.4. Identification Tests**

We hypothesized that replacing dirt floors interrupted the transmission of parasitic infestations and that this led to the improvements observed in the other outcome indicators. However, there could have been other state-specific changes that coincided with the implementation of the _Piso Firme_ program in Coahuila and that are at least partially responsible for the outcomes observed. For example, improvements could have been made in the health-care system in the State of Coahuila, but not in Durango.

In this section, we offer evidence that rules out the possibility that the results presented in the previous section are the consequence of confounding causes. We first consider other diseases that would presumably not have been affected by the replacement of dirt floors with cement floors: respiratory diseases and skin lesions. We then also 19
whether there is a persistent effect of the savings default. Results for dependent variables are in levels of Malawi kwacha (MK) to facilitate the interpretation of the economic importance of treatments.

Next, we look at spending in several categories, one and two weeks after the transfer. Recall that expenditure data were collected before prizes were announced (survey round 1). Specifications incorporate the pre-treatment value of outcomes and fixed effects as in equation (1). For outcomes measured with survey data, we use OLS to estimate separately using data one week after the transfer (Panel A) and two weeks after the transfer (Panel B). We collected two-week follow up data only for the subset of respondents who received transfers immediately or with one day delay, so the sample size in Panel B is smaller than in Panel A, and differences between the point estimates of coefficients in the two panels reflect both the difference in the sample and any change in the impact over time.

Finally, we study the effect of payment delay on expenditures. The dependent variable is expenditures one week post-transfer since using the two-week follow up data would preclude using the eight day delay treatment group. The specification we run is with village and week-of-first-survey fixed effects and standard errors computed as in equation (2). Here, _β_ 1 is the marginal effect of a one day delay compared to an immediate payment, while _β_ 2 is the marginal effect of an eight day delay instead of immediate payment, averaged across cash and direct deposit treatments.

# **5 Savings default results**

## **5.1 Administrative outcomes**

Estimates of equation (1) test whether the savings default had any impact on immediate cash-on-hand. Since we argue that the transaction costs associated with receiving the full transfer in (or converting it to) cash were equal, the mode of payment should affect outcomes only through psychological channels. Differences in bank activity for cash compared to direct deposit recipients is sufficient (though not necessary) evidence that savings defaults matter.

Table 4, Panel A estimates the effect of windfall income and savings defaults on deposits. For recipients defaulted into saving, the total deposits of MK 25,089 (the sum _α_ + _β_ , from 10
controls, which absorb little of the variation, and Column (3) shows the fully-specified regression including student controls. These controls account for a further point in the student performance. While we refrain from ranking management indices across countries, Figure 5 plots the coefficients of country-level regressions of management on PISA math test scores using the specification of Column (1). The estimation loses precision once we restrict to individual country samples but still broadly supports the positive relationship found in Table 1.

For the Prova Brasil student-level dataset, we run standard OLS regressions, also clustering the standard errors at the school level. Results are reported in standard deviations. In these Prova Brasil specifications, we include state fixed effects, and successively introduce school controls (student-teacher ratio, log of the number of students, dummy variables indicating the presence of an IT lab, science lab, and library, a dummy for male principals, dummies for educational attainment, and dummies for experience as principal), and then student controls (gender, race, socio-economic status, and mothers’ educational attainment). Column (4) shows the raw relationship between the Prova Brasil-based school management index and student performance, only controlling for state fixed effects. One standard deviation higher score in the management index is strongly correlated with 0.068 standard deviations higher Portuguese scores and 0.078 standard deviations higher math scores. Column (5) shows that including school characteristics absorbs very little of the variation, while Column (6) shows that including student characteristics absorbs only slightly more. The fully specified regression supports the general positive relationship between the school management index and student performance.

# **3 Why does management matter in schools?**

There are myriad uses of our new indices. In this paper, we push the frontier of understanding the mechanisms behind the management and performance relationship by focusing on teachers. Our aim is not to provide a theoretical contribution _per se_ , but rather to formalize a policy discussion around teacher incentive and selection mechanisms and their relationship to management practices and student performance. We take wider system-level factors—in particular hiring and firing autonomy, admissions autonomy and competition between schools—as given and assume that teachers and 11
shift student outcomes and teacher mindsets in Indonesia?” _Impact Evaluation Report_ . Washington, DC: World Bank.

- Johnson, H. C., and N. Muller. 2022. “Dreams and Barriers: Aspirations, Expectations, and Schooling Outcomes of Indonesian Students”. _World Bank Policy Research Working Paper_ 10267. Washington, DC: World Bank.

- Kosec, K., and C. H. Mo. 2017. “Aspirations and the Role of Social Protection: Evidence from a Natural Disaster in Rural Pakistan.” _World Development_ 97: 49–66.

- La Ferrara, E. 2016. “Mass media and social change: Can we use television to fight poverty?” _Journal of the European Economic Association_ 14 (4): 791-827.

- ———. 2019. “Aspirations, Social Norms, and Development.” _Journal of the European Economic Association_ 17(6): 1687–1722.

- Laajaj, R., A. Moya, and F. Sanchez. 2022. “Equality of opportunity and human capital accumulation: Motivational effect of a nationwide scholarship in Colombia”. _Journal of Development Economics_ 154: 102754.

- Lim, J., and J. Meer. 2020. “The Impact of Advisor Gender on Female Students STEM Enrollment and Persistence”. _Journal of Human Resources_ 55(3): 809–835.

- Londoño-Vélez, J., F. Sánchez, C. Rodriguez, and L. E. Alvarez. 2024. “Elite Colleges as Engines of Upward Mobility: Evidence from Colombia's Ser Pilo Paga”. _Manuscript_ . Available at: <u>http://bit.ly/3U5g1XD (2023 version as NBER Working Paper 31737).</u>

- Lybbert, T. J., and B. Wydick. 2018. “Poverty, Aspirations, and the Economics of Hope.” _Economic Development and Cultural Change_ 6(4): 709–753.

- ———. 2022. “Hope and Poverty in Development Economics: Emerging Insights and Frontiers” _CEGA Working Papers_ 211 ( _forthcoming in_ A. Scioli and S. van den Heuvel (eds.) _The Oxford Compendium of Hope_ ). UC Berkeley.

- Macours, K., and R. Vakis. 2014. “Changing Households’ Investment Behaviour through Social Interactions with Local Leaders: Evidence from a randomized transfer program.” _Economic Journal_ 124(576): 607–633.

- ———. 2019. "Sustaining Impacts When Transfers End: Women Leaders, Aspirations, and Investment in Children." _in C. B. Barrett, M. R. Carter, and J. P. Chavas (eds.) The Economics of Poverty Traps. University of Chicago Press._

- McKenzie, D., A. Mohpal, and D. Yang. 2022. “Aspirations and Financial Decisions: Experimental Evidence from the Philippines.” _Journal of Development Economics_ 156: 102846.

- Molina, T., and E. Usui. 2023. “Female Labor Market Conditions and Gender Gaps in Aspirations.” _Journal of Economic Behavior and Organization_ 211: 165-187.

- Moya, A., and M. Carter. 2019. “Violence and the Formation of Hopelessness and Pessimistic Prospects of Upward Mobility in Colombia.” _World Development_ 113: 100–115.

31
Policy Research Working Paper 7943

# **Abstract**

Many African countries rely on sporadic land transfers from customary to statutory domains to attract investment and improve agricultural performance. Data from 15,000 smallholders and 800 estates in Malawi allow exploring the long-term effects of such a strategy. The results suggest that (i) most estates are less productive than smallholders; (ii) fear of land loss, although not exclusively due to estates, is associated with a 12 percent productivity loss for females, which is large enough to finance a low-cost tenure regularization program; and (iii) failure to collect realistic land rents implies public revenue losses of up to US$50 million per year.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org; xia.fang.fx@gmail.com; and stein.holden@nmbu.no.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure A2 – Educational Paths – Females <!-- Start of picture text --> Compulsory Education Secondary Edu Higher Education<br>General 4y- Post<br>Primary Preparatory<br>Secondary University Grad<br>5y-<br>3y-Voc. Agr. University<br>Post<br>Secondary<br>3y-Voc. Ind<br>3y-<br>Voc.Com<br>5y-Voc Edu<br>15.7 7.6 -1 8.2 7.7 8.1 16.5 6.4 7<br>3.9 10.9 10.2 13.4 14.3<br>Labor Market -Rates of Return (%) – Females 2006<br><!-- End of picture text --> 35
# **4.3.1 Validity of the parallel-trend assumption**

Recall that a crucial assumption underlying the difference-in-differences approach is that units of the treatment and control groups were following a “parallel trend”, so that outcomes of the control would reasonably serve as counterfactuals for the treated units after the Formosa disaster took place. We have discussed descriptive evidence from Figures 3 and 4, and Table A1. Specifically, we show that both measures of fishery income (Figure 2) and boat density (Figure 4) in the treatment and control groups seem to follow a common monthly pre-trend. Additionally, in Table A1, while we detect a significant reduction in means between the preand post-treatment earning averages for _Formosa_ -affected fishery workers in 2016, we find no such statistical differences for the control groups. In this sub-section, we conduct additional empirical exercises to further support the validity of the parallel-trend assumption.

In the first exercise, we perform a placebo test using the baseline data from LFS 2015, and generate a fictional event in April 2015. Because this entire time frame predates the actual _Formosa_ disaster, we do not expect any impact on the fishery industry in the central coast (i.e. in Ha Tinh, Quang Binh, Quang Tri, and Hue). Panel A in Online Appendix Table A3 documents the DiD estimates under this placebo test. It is evident that relative to the control group, the differential change in fishery incomes before and after April 2015 are statistically indistinguishable from zero.

We repeat an identical falsification exercise for fishing activity’s outcomes (i.e. boat detection likelihood and density) in Table A4, using an antecedent sample of grid observations for the months between 2013 and 2015, in which a fictional event is imposed hypothetically in May 2014. Everything else remains similar to the regressions in Table 3. As can be seen, all of the estimated DiD coefficients are small and statistically insignificant, with only one exception in column 6. In this column, _β_<sup>ˆ</sup> 1 was actually _positively_ estimated; however, the estimate is small in magnitude (3 percent) and barely significant at the conventional 10% level.

In Panel B of Table A3, we provide a direct test for the validity of the “industry control group” in the main DiD analysis. Recall from Table 2 that individuals who lived in the _Formosa_ -affected provinces and worked in manufacturing, construction and retail serve as the counterfactuals for fishery workers, the average monthly earnings of these individuals should not be affected by _Formosa_ . We test for this hypothesis by comparing these individuals’ before-after earning differentials to that of workers living in provinces unaffected by _Formosa_ 16
# **Figure 2. Data Representativeness**

<!-- Start of picture text --> Maturity Data Representativeness Purpose Data Representativeness<br>2004 2006 2008 2010 2012 2014 2004 2006 2008 2010 2012 2014<br>World Private Credit (IFS) Sample: World Private Credit (IFS) World Private Credit (IFS) Sample: World Private Credit (IFS)<br>Sample: Sum of Maturities Triples Sample: Sum of Maturities Pairs Sample: Sum of Purpose<br>Note:Sampling within countries averages 97%, and across across averages 44% Note:Sampling within countries averages 99%, and across across averages 60%<br>60 60<br>40 40<br>Credit (USD tn) 20 Credit (USD tn) 20<br>0 0<br><!-- End of picture text --> 23
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ residual value added is reduced. That is, for a small trading economy, the terms of trade affects the value added of production.<sup>21</sup>

## **Table 5: Regression Estimation Results of a Factor Productivity Model for the Cote d'Ivoire, 1960-2002**

**<u>Dependent Variable: First Differences of Natural Logarithms of Real Output per Worker</u>**

|**Indépendant Variables**|**Coefficient**|**T-Statistic**|
|---|---|---|
|DeltaLog, CapitalperWorker|0.56473|4.31|
|Delta,Average Schooling perWorker|0.05528|0.32|
|DeltaLogTerms of Trade|0.10019|2.77|
|Constant|-0.00465|0,17|

Durbin-Watson Statistic: 1.9607 R-Squared: 0.52 F(5,36) = 13.5 ADF test <u>on residual: Z(t) = -6.153 > 1% Critical Value</u>

**A one percent change in the rate of change in terms of trade results in a one-tenth of a percent change in the measured real value of per capita output.** Table 5 presents the coefficient estimates, measures of “goodness of fit”, and tests of certain hypotheses about parameter values, for a regression “explaining” the rate of growth of output per worker as a result of the growth rate of physical capital per worker, the increase of average schooling per worker (representing growth in human capital), and the rate of change of the terms of trade.<sup>22</sup> The rate of change of the terms of trade is taken as an important determinant of or proxy for Total Factor Productivity<sup>23</sup> . A one percent change in the rate of change in terms of trade results in a one-tenth of a percent change in the measured real value of per capita output. A dummy variable added for the internal conflict in 2000-2002 provided an estimate that conflict knocked -3.8% off the expected growth rates for those three years, for a cumulative effect of 11% for the three years.

# **5. Concluding Remarks**

The preceding analysis suggests several conclusions, an important policy implication for diversification, and some avenues for further research on Cote d’Ivoire’s economy.

**First, Cote d’Ivoire’s  long-term per capita growth performance is not fully explained by either the over-valuation of the CFA in late 1980s and early 1990s, nor by the recent conflict.** While the impact of the conflict was severe––especially on the social front––longer term factors such as capital accumulation, total factor productivity, and terms of trade are key to understanding the country’s performance over these four

> 21 For a trading nation, the prices of exports and imported inputs already appear in the national income accounting statistics used to compute TFP.  In the Annex 1, we first present an intuitive explanation, and then an algebraic manipulation of national income identities to show the link between the terms of trade and TFP.

> 22 The coefficient point estimates for the contributions of physical capital and human capital (schooling) differ somewhat but not significantly from the values often assumed in the literature, and from those which were assumed in the accounting TPF estimate presented earlier.

17