rafmacalaba/datause-encoder-data huggingface dataset:
In other words, it appears that in MENA, the decision of how many different types of green investments to make depends on both credit constraints and the quality of green management practices, once the estimates account for endogeneity.

**_Table 6: Ordered logistic IV regression for the number of different green investments (average marginal effects)_**

||No green<br>investment|One|Two to four|Five or more|
|---|---|---|---|---|
|||_Panel A: Ov_<br>|_erall sample_<br>||
|Credit constraints|0.265*|0.043*|-0.078*|-0.230*|
||(0.117)|(0.020)|(0.039)|(0.100)|
|Green management|-0.352***<br>(0.043)|-0.057***<br>(0.012)|0.103***<br>(0.025)|0.306***<br>(0.040)|
||_Pa_<br>|_nel B: Allow all coeff _<br>|_icients to vary by regi_<br>|_on_<br>|
|Credit constraints ECA|0.283**|0.066**|-0.065*|-0.285**|
||(0.108)|(0.025)|(0.026)|(0.108)|
|Credit constraints MENA|0.415*|-0.025|-0.203*|-0.186*|
||(0.206)|(0.014)|(0.101)|(0.092)|
|Green management ECA|-0.313***|-0.073***|0.072***|0.315***|
||(0.037)|(0.009)|(0.009)|(0.037)|
|Green management MENA|-0.398***|0.024***|0.195***|0.179***|
||(0.054)|(0.006)|(0.025)|(0.025)|
|Observations||17,|902||
|Clusters||3,|022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the number of green investments, based on ordered logistic IV regression with the categories based on the number of green investments as a dependent variable. Regression controls for the locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); indicators for no firms in other sectors in a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

Taken together, these estimates suggest that policy measures that ease access to bank credit specifically for green investment might not suffice to see a substantial increase in green investments. This appears to be the case particularly for MENA and may be relevant for other countries at a similar level of development and climate change awareness. Governments and development banks should also consider measures that could strengthen green management practices. These may include disseminating information on best green management practices, requirements to measure and report environmental impacts or credit lines contingent on the adoption of better green management practices by firms.

28
the 3 coefficients add up to zero is 0.453). In Column 3 we control for the type of transaction performed and results are virtually unchanged.

# **6. Discussion**

Our results from both the Democratic Republic of Congo and Senegal provide two pieces of evidence indicating that gender matters in agent banking transactions. First, both male and female clients exhibit assortative gender matching. Perhaps surprisingly, for female clients this preference for female agents seems largely unimpacted by variation in the availability of female agents. When we restrict our analysis to markets where at least 25% of agents are female, same gender preference does not increase, as would be expected if convenience were the driving mechanism. This suggests that gender matching for females is not mainly driven by what Barr et al. (2012) describe as ‘opportunity’, i.e. the availability of female agents. Rather, the behavior is more consistent with the notion of ‘homophily’ as customers exhibit a stable preference to transact with agents of their gender.

Second, we use the transaction amounts and account balances to explain gender homophily. We find that female clients are more likely to use female agents the larger their transactions and account balances. We thus posit that gender-based trust may be driving the observed gender homophily as female clients have a stronger tendency to use female agents for larger transactions: larger amounts are riskier, and thus when the stakes are higher, female clients rely more heavily on female agents. Table 7 shows that both men and women transact higher average amounts at agents of their gender (men USD$250 and women USD$221). When visiting agents of the opposite gender, the average female transaction declines to USD$133 (a 40% reduction) while transactions of males only show a 17% reduction (to USD$207).

The balance data provide additional support for our conjecture that gender-based trust could be an important underlying mechanism. Since agents know the customer’s balance when a transaction is made, there is a risk that financial information is disclosed to others without the client’s consent. Visiting a female agent can be a strategy for women to mitigate this risk since female agents may be less likely to disclose information to other clients, compared to male agents.

# **7. Conclusion**

The research findings have practical implications for financial service providers. While the preference for same gender agents appears to be similar in strength between males and females, the implications vary along gender lines. Since females prefer female agents but only 23% of agents are female, female customers will have to travel farther on average to transact with an agent of their gender. The underrepresentation of female agents may thus represent a barrier to greater financial inclusion, 15
Overall, the members of treatment households were significantly more likely to be engaged in remunerated work (Table 2, column 1). The share of household members who had worked for cash in the last 12 months was about 11 percentage points higher in treatment households than the control mean of 33 percent.<sup>20</sup> The share was slightly more pronounced among rural households than urban households (12 percent vs. 10 percent).

Program participation also crowds in labor force participation among adult household members over and above the direct PW program participation. In treatment households, the employment of nonparticipating household members increased (Table 2, column 3).<sup>21</sup> The share of nonparticipating household members who had worked for cash during the last 12 months was 28 percent in the treatment group and 22 percent in the control group. The rise in the share of household members who were not participating in the program, but who were participating in paid work was larger in rural areas than in urban areas (7 percentage points vs. 4 percentage points). There was no evidence of short‐term impacts on the incidence of child labor in rural or urban areas.

Participation in the program led to a net increase in labor force participation among women in treatment households (Table 2, column 2). This is also not remarkable given that the CfW program rules included a predefined quota of at least 30 percent participation by women. About 46 percent of the women in treatment households had worked for cash in the last 12 months, compared with 35 percent in the control households. The rise in female labor force participation within households was almost double in rural areas compared with urban areas (14 percentage points vs. 8 percentage points) even though the two areas started at a similar control mean of 35 percent.

One interesting auxiliary impact of program participation was an increase in reported migration among the treated households (Table 3). A rise in both in‐ and out‐migration—individuals moving, respectively, into or out of households from another town or city—was observed, but the overall impacts were small. Among both rural and urban households, out‐migration was somewhat greater than in‐migration, although the difference was wider in rural households. Greater migration seems intuitively like a natural outcome of expanded economic potential in areas where job opportunities are scarce.

**B. Impacts on Household Income, Expenditure, Savings, and Investment**

# **_B.1 Income Effects_**

Given the large program impacts on household economic activity, the clear income effects are not surprising. The treated households had higher reported income: on average, the reported cash income during the previous month increased by 51 percent. However, the value of reported cash and in‐kind income taken together rose by only 29 percent (

> 20 In this paper, all recall periods are measured from the date of the household survey, unless indicated otherwise.

> 21 Substantial increases were observed both in households in which the beneficiary is a man and in households in which the beneficiary is a woman.

11
increases for longer closures. Students with an immigrant background lost 0.33 points with every week of full school closures and while in countries with shorter closures their learning loss was smaller, for countries with around 25 weeks of closures their learning loss is equal to that for natives, but it increases for longer closures.

# **Table 3. Impact of Pandemic on Student Mathematics Achievement by gender and**

# **socioeconomic status**

||Overall impact|Impact by length<br>of full school<br>closures<br>(UNESCO)|
|---|---|---|
|Departure from linear trend in 2022|-14.20***|-10.00***|
|Departure from linear trend in 2022 for boys (girls=baseline)|4.18***|6.83***|
|Departure from linear trend in 2022 for immigrant students<br>(natives=baseline)|4.98***|8.00***|
|Departure from linear trend in 2022 for the slope of ESCS|2.62***|3.78***|
|Change in the departure from linear trend in 2022 by:|||
|- Length of closures (in weeks)||-0.29***|
|- Length of closures*Boys||-0.17**|
|- Length of closures*Immigrant||-0.33***|
|- Length of closures*ESCS slope||-0.12***|
|_Additional controls_|||
|Individual level: gender, immigrant background, SES|Yes|Yes|
|Country average: gender, immigrant background, SES|Yes|Yes|
|country fixed effects|Yes|Yes|
|country*time|Yes|Yes|
|Country*time*boys|Yes|Yes|
|Country*time*immigrant|Yes|Yes|
|Country*time*ESCS|Yes|Yes|
|N|2,896,916|2,896,916|

After the pandemic, the relationship between achievement and student socioeconomic background became slightly stronger. The slope of the ESCS index measuring socioeconomic status in PISA increased by 2.62 points. In PISA 2012, the last time mathematics was the main domain before the pandemic, the OECD average slope was around 39 points. Thus, the pandemic increased socioeconomic inequality as measured by the ESCS regression slope by around 7 percent. On the other hand, every week of full school closures diminished this increase 14
<!-- Start of picture text --> 2<br>;<br>‘Poverty line<br>15<br>2<br>5 al<br>ia)<br>.05<br>0<br>0 19 5 . 10, 15 20<br>Daily consumption per capita (2011 USD PPP)<br>MN 2016 (last survey) [J 2021 (nowcast)<br><!-- End of picture text -->
# Table 4: Regressions of the Land Value of Irrigated and Rainfed Farms

||Rainfed||Irrigated||
|---|---|---|---|---|
|Variable|Coef.|T-stat.|Coef.|T-stat|
|Intercept|-3406|-4.43|2083|1.34|
|Temperature summer|344|3.62|235|1.40|
|Temperature summer squared|-13.8|-4.94|-6.69|-1.24|
|Temperature winter|353|6.82|-317|-2.17|
|Temp winter squared|-9.17|-5.63|6.64|1.42|
|Precipitation Summer|7.53|3.54|8.77|1.28|
|Precipitation Summer Squared|-0.01|-2.33|-0.01|-0.51|
|Precipitation Winter|0.06|0.03|-13.63|-1.90|
|Precipitation Winter Squared|0.01|0.69|0.06|1.81|
|Temp summer*Large Farms|105|1.48|-531|-2.03|
|Temp summer Sq*Large Farms|-0.56|-0.23|15.47|1.72|
|Temp winter*Large Farms|-144.7|-1.86|27.2|0.08|
|Temp Winter Sq*Large Farms|2.26|0.88|2.02|0.17|
|Prec summer*Large Farms|-3.75|-1.13|30.49|1.85|
|Prec summer Sq*Large Farms|0.01|1.01|-0.12|-1.99|
|Prec winter*Large Farms|-5.02|-1.48|26.14|1.59|
|Prec winter sq*Large Farms|0.00|-0.34|-0.11|-1.56|
|Soil Cambisols|5.15|1.42|-0.89|-0.08|
|Soil Ferrasols|10.01|4.03|22.64|3.45|
|Soil Phaeozems|2.12|0.88|26.73|3.74|
|Soil Luvisols|14.63|4.74|-12.53|-1.40|
|Soil Arenolsols|16.59|3.27|17.49|1.05|
|Soil Regosols|-8.04|-2.57|-8.77|-0.96|
|Soil Vertisols|20.54|5.03|9.42|2.13|
|Soil Yermosols|19.20|0.63|26.28|2.42|
|Altitude|-0.09|-0.79|-0.51|-2.15|
|Electricity dummy|585.4|4.03|1337.1|2.54|
|Computer dummy|445.1|3.78|105.8|0.43|
|Texture(Mixed)|534.1|4.00|1997.4|4.57|
|Texture(Clay)|-1089|-6.55|-961.8|-1.83|
|Age of the head|-0.26|-0.10|-23.01|-2.75|
|Female dummy|-272.5|-1.62|292.7|0.70|

30
# **Figure 1. Investment growth slowdown**

_Investment growth in EMDEs has slowed sharply since the global financial crisis. In 2015, the share of EMDEs with investment growth below its long‐term average reached its highest level excepting global downturns. Long‐term forecasts suggest continued weakness in investment growth. The investment growth differential between EMDEs and AEs has narrowed._

## **A. Investment growth**

## **B. Share of EMDEs with investment growth below its long‐**

## **term average**

<!-- Start of picture text --> Percent<br>1990-2008 average<br>12<br>2003-08 average<br>8<br>4<br>0<br>EMDEs AEs World<br>C. Catch‐up to U.S. per capita incomes<br>Number of years 1993-2008<br>120 2003-08<br>2013-15<br>100<br>80<br>60<br>40<br>20<br>0<br>EMDEs EMDE EMDE<br>commodity commodity<br>exporters importers<br>Percentage points<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text -->

## **E. Difference between AE and EMDE investment growth**

<!-- Start of picture text --> Percent of EMDEs<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text -->

## **D. Five‐year‐ahead forecasts of investment growth**

<!-- Start of picture text --> Percent<br>EMDEs AEs World<br>10<br>8<br>6<br>4<br>2<br>0<br>F. Share of world investment<br>Percent EMDEs excluding BRICS<br>Other BRICS<br>50 China<br>40<br>30<br>20<br>10<br>0<br>1993-1999 2000-08 2010-15<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Consensus Economics; Haver Analytics; International Monetary Fund; Oxford Economics; World Development Indicators, World Bank.

A. Weighted averages. Long‐term average starts in 1991 for EMDEs due to lack of earlier data.

B. Long‐term averages are country‐specific and refer to 1990‐2008. Latest year is 2015.

C. Number of years needed to catch‐up with 2015 real per capita GDP level in the United States, assuming average growth rates over each period denoted for each group.

D. Each line shows five‐year‐ahead Consensus Forecasts as of the latest available month in the year denoted. Unweighted averages of 21 EMDEs and 25 advanced economies. World sample includes 46 countries. Last observation is for October 2016. E. The sample includes 100 EMDEs and 34 AEs. Difference between EMDEs’ and AEs’ weighted average investment growth rates.

F. Each column shows the period average of the share of global investment contributed by each respective group denoted. The world sample includes 100 EMDEs and 34 AEs.

27
20 a standard multi-country, multi-sector model to show that the new GVC measures better predict the impact of trade shocks on output than traditional openness indicators. Section S4.2 documents a positive correlation between GVC participation and per capita income growth, with the new measures containing more informational content than existing ones.

# **Methodology and Empirical Estimation Details**

Our objective is to quantify how different dimensions of GVC participation affect the output response to demand-side fluctuations. The empirical deliberately builds on well-established empirical frameworks. It comprises three steps.

**_Step 1:_** _Estimating Exogenous Demand Innovations._ We estimate innovations in countrylevel demand using global Input-Output (I-O) data via a fixed effects model adapted from Alfaro et al. (2021) and Kramarz et al. (2020). Specifically we estimate:

Here, ∆ _yij,t_<sup>_r_indicatethechangeinfinalsalesfromcountry</sup><sup>_i_tocountry</sup><sup>_j_insector</sup><sup>_r_,</sup> expressed in percentage terms. The term _ηj,t_ captures the demand innovations in the importing country _j_ at time _t_ , while _γi,t_<sup>_r_accountsforexportingcountry-sector-timeeffects.16</sup> We interpret _ηj,t_ as a residual innovation in final demand that reflects both unexpected fluctuations and persistent shifts. Although our terminology refers to ”shocks,” these innovations may also include trend-like movements. Our interest lies in understanding the implications of exposure to such changes—regardless of whether they are transitory or structural—for output volatility.

**_Step 2:_** _Constructing Direct and Indirect Demand Shocks._ Using a shift-share instrument approach that builds on Ferrari (2023), we aggregate _ηj,t_ into sector- and country-specific shocks based on observed trade patterns:

- The _direct foreign demand shock_ for country _i_ , sector _r_ , at time _t_ , is:

where _φ_<sup>_r_</sup> _ij,t−_ 1<sup>isthelaggedshareoffinalsalesfrom</sup><sup>_i_to</sup><sup>_j_insector</sup><sup>_r_.17</sup>

- The _direct domestic demand shock_ for country _j_ is simply the demand innovation _ηj,t_ , applied when _i_ = _j_ .

- The _direct demand shock_ variable used in Table 1, Column (5), is the sum of the foreign and domestic components, interacted with the share of output not related to GVCs.

- The _indirect demand shock_ aggregates demand changes _ηj,t_ through downstream linkages based on IO structures (captured by weights _ψij_<sup>_r_thatmeasuretheexposureof</sup>

> 16Results throughout the paper are robust to the inclusion of different sets of fixed effects in the estimation of demand innovations. The estimation is carried out by excluding country _i_ from the sample when computing the _jt_ demand innovation that might affect it, to reduce concerns of endogeneity.

> 17 _φrij,t_<sup>isequalto</sup><sup>_y_</sup> _ij_<sup>_r/_�</sup><sup>_G_</sup> _j_<sup>_y_</sup> _ij_<sup>_r_;notethat</sup><sup>_φr_</sup> _ii,t_<sup>= 0.</sup>
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Ever Had Sex|S4|Core<br>Respondent|No|2,3,4|=1 if core respondent ever had sex at time of<br>household survey. Variable is corrected for<br>inconsistences across rounds.|
|Number of Sexual Partners<br>(lifetime)|S4|Core<br>Respondent|No|2,3,4|Number of lifetime sexual partners self-<br>reported by core respondent.|
|Sexually Active During<br>Past 12 Months|S4|Core<br>Respondent<br>|No|2,3,4|=1 if core respondent sexually active during 12<br>months prior to household survey<br>Core respondents age at first sexual activity,|
|Age at First Sex<br>Older Partner<br>Condom Use|S5<br>S5<br>S5|Core<br>Respondent<br>Core<br>Respondent<br>Core<br>Respondent|No<br>No<br>No|2,3,4<br>2,3,4<br>2,3,4|reported for the sub-sample that report having<br>ever had sex at time of household survey.<br>=1 if core respondent reports an older partner.<br>A core respondent is defined as having an<br>older partner if she has had a partner who is 5<br>years older or more in the past 12 months.<br>Variable is defined for the sub-sample that<br>report having ever had sex at time of<br>household survey.<br>=1 if core respondent uses a condom. 'Condom<br>Use' is defined as using a condom at last sex<br>with most recent sexual partner. Variable is<br>defined for the sub-sample that report having<br>ever had sex at time of household survey.|
|Psychological Distress|S6|Core<br>Respondent|No|2,3,4|=1 if core respondent suffers from<br>psychological distress. Psychological distress<br>is equal to one if the summed General Health<br>Questionnaire- 12 score is equal to three or<br>higher, and is zero otherwise.|

64
# **Figure 2: Illegal Foreign Workers Apprehended**

<!-- Start of picture text --> 70000<br>60000<br>50000<br>40000<br> Female<br> Male<br>30000<br>20000<br>10000<br>0<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Japanese Ministry of Justice.

44
# DISORGANIZATION OR **SELF-** ORGANIZATION?1

## FRANCESCA RECANATINI AND RANDI RYTERMAN<sup>2</sup>

The transiton from plan to market provides a rare opportnity for insight into the endogenous development of economic institutions. Economic actvities during the Soviet regime were coordinated by a central authority. These coordintg mechanisrs were disrupted duing the transition period, leading to an increase in the transaction costs for firms. Blanchatc and Krerner (1997), among others, emphasize the negative impact of this "disorganion on output behavior at the beginning of transition. Though this argument is correct, we believe that this and simlar works stop short of a fuler characterization of transition by concentrating only on the disruptive dfects of the reforn process.

This paper begins where the former works end by exanmining one of the key institutons that have energed spontaneously in response to the challenges of transition: business associations. Its main contribution is to provide enpirical evidence that institutions that help coordinate production and trade spontaneously emnerge in an enviromment characterized bywidespread 'disorganization". Using a lagely unexplored, firm-level data set, ve document the emergence of business associations at the beginning of transition and provide evidence that these new coordinating institutions mitigated the intial output decline. Building on the growing literature on transaction costs and comnplexity, we interpret the emergence of these infornal institutions as the firns' rational response to coordinate activites in a decentralized economy.

Keywords: Coordination Problems, Transition, Alternative Institutions JEL classificatio **n** os.: D23, L10, 017, P21, D71 I We would like to thank Lee Benhamn, John Haiwager, Karia Hoff, Robert Klitgaard, Peter Murrell, John Shea and seminar participants at the World Bank the University of Maryland, SEA, the 1999 ISME Conference and the 3ri Conference on "Institutions and Transition" for their many insights and comments. We are also grateful to Bill Evans and Bob Schwab for sharing their comnputep **r** ogram, and _to_ Ouiner Blanchard and Michael KRemer for sharing their dama. Any remaining errors are our ownL The vews expressed here are those of the authors and not of the World Bank or its memnber govemments. 2 The World Bank 1sis H Stett, NW Washington DC 20433; email: frecanatnQ_worldbank.org Mrtermanat0.otldbatnk.mr. e paragraphs, 2000 by Francesca Recanarini nay be quoted without and Randi RyemL explicit permission provided that ful redit, including All rights reserved. Short sections of text, not to exceed two0 notice, is given to the source.