rafmacalaba/data-use-mentions-extended huggingface dataset:
We group these responses into the behavioral manifestations of political and social engagement. We also asked respondents a series of questions about their perceptions of their political and social status. For example, we asked respondents about their sense of personal agency, their ability to contribute to their community, the quality of their relationships, and their trust in a number of organizations and institutions. We list which indicators were asked in each survey in Table 2. We provide additional details on the specific question wording analyzed in the Appendix, Section B.

As noted above, self-reported measures of political and social behaviors may have a number of biases. We aim to limit these through our enumeration strategy and survey design. But bias likely remains. The structure of our survey data limits our ability to directly observe respondents’ motivations for their participation in some of the manifestations of empowerment. IDPs may report their intention to vote, for example, but have no way to note that they only vote due to clientelism which is pervasive in some of the contexts we study. While we cannot distinguish between such motivations, we expect that IDPs and vulnerable but not displaced communities face similar incentives.

|||**Behav**|**ioral**||||**Perception**||
|---|---|---|---|---|---|---|---|---|
|**Country**|Voting|Participate Civil Society|Talk Openly|Attend Meetings|Trust|Personal agency|AbilityContribute Community|Relationships|
|Iraq|||||||||
|Philippines|||||||||
|Uganda|||||||||

Table 2: Manifestations of Political and Social Engagement Captured in Surveys Our explanatory variables measure displacement status. First, we measure whether a respondent is displaced or has a history of displacement. We describe the specific measures we
by asking how laws facilitate transactions. They examine the significance of legal institutions in the transacting process. Their analysis suggests that firms that invest in constructing contracts and have better knowledge of the law have better chances to successfully complete a transaction. To illustrate the potential advances in research on transaction costs, consider the example of credit access. The World Bank's efforts to collect data on this particular type of costs has already allowed researchers to explore more closely the theorized link between credit access and firm performance. Fan, Lee and Schaffer (1996), for example, analyze the impact of the financial sector reform in Russia on firms in the early years of transition. Their data, collected at the end of 1994, shows that a partial reformn of the banking system, with many banks still acting "soft" in their dealings with firms, slowed the enterprise restructuring process amd undermined financial sector reform. Bank loans are not the firm's only source of financing, especially when credit is rationed or the credit market is underdeveloped. A recent study on Zimbabwean manufacturing firms using the RPED data (Fafchamps, 1996) suggests that trade credit is an important, alternative source of short-term liquidity for many firms.

# F. **The Role of the State**

Even in industrialized countries, the state is generally the most importaint single actor in the economy. The policies the government implements can both harm and benefit economic growth. As Stiglitz (1998) notes, "there is a growing consensus that governments can play a vital role in successful development efforts, but we also recognize that the wrong kind of government intervention can be highly detrimental." Surveys of firms can therefore be an important tool in determining which aspects of government involvement are beneficial and which are harmful.

# **Regulation**

Despite its potential importance in explaining different growth rates across nations, little is known empirically about regulation in developing countries. For instance, 'what are the effects of regulation on firm performance, employment, and pricing
Finally, we use a reduced version of the human asset index taken from Feindouno and Goujon (2019) (28). Their HAI is a simple composite index from 0 (low score) to 100 (best score) which includes four subcomponents: i) un undernourishment index, ii) the under five years old mortality index, iii) the adult literacy rate index, and iv) the secondary school enrollment index. We recompute the HAI by excluding the adult literacy rate index as it is difficult to interpret this indicator in post-war situation for CAR due to the significant number people registered as international refugees (744, 000 or 13.3 percent of the population at end of 2022), mostly in neighboring countries, comprising an unknown proportion of literate and illiterate adults. The reduced version of HAI is available from 1990 (the first year available for CAR) up to 2014, which enables us to only capture the short-term impact of the civil war during the rule of Michel Djotodia as the President of the Republic after the Séléka rebel coalition took over Bangui.

Table A1 lists the countries included in the synthetic controls, along with their weights. It also provides pre-war and post-war outcome values in CAR for the synthetic controls. Additionally, this table displays CAR MSPE in the pre-war period for each type of outcome, offering a comprehensive overview of the synthetic controls' performance.

# **Empirical results**

CAR has faced a persistent lack of economic growth since gaining independence in 1960. The country’s history of political instability is characterized by a series of coups, which led to a fragile institutional framework and a decline in overall productivity. This decline is noticeable in the absence of structural transformation, limited technological progress and innovation, and poor management of both public and private enterprises. Moreover, the country’s GDP _per capita_ in 2012 was equivalent to the level recorded in 1990, highlighting the stagnation that had plagued the nation for over two decades (see Figure 1).

In addition, the subsequent
# **I. Introduction**

A growing body of research suggests that low levels of cognitive development in early childhood have adverse long-term consequences for adult wellbeing. Research based on the 1958 British cohort study shows that performance on mathematics and reading tests at age 7 predict test scores at later ages, as well as educational attainment and wages as adults (Connolly et al., 1992; Currie and Thomas, 1999; Robertson and Symons, 2003). Evidence from a later-born cohort indicates that tests administered as early as 22 moths of age are also associated with adult education outcomes (Feinstein, 2003). Several of these authors find that the predictive effects of test performance in childhood on adult education and wages are particularly large for children from households with lower socioeconomic status (SES) (Currie and Thomas, 1999; Feinstein, 2003).<sup>1</sup> These results underscore the importance of understanding the determinants of cognitive development, especially among poor children.

Research on both industrialized and developing countries has focused on identifying environmental factors that affect cognitive development. A common finding in the US literature is that higher SES, as measured by income, wealth, or parental education, is associated with better cognitive development of children.<sup>2</sup> The extent to which this association is causal is a topic of live debate. More resources for children, better parenting skills, increased cognitive stimulation of children, and lower incidence of maternal depression and stress have all been proposed as “pathways” from high SES to high levels of cognitive development of children.

In contrast with the US literature, research on developing countries has placed greater emphasis on child health as a determinant of cognitive development and little emphasis on parenting. Numerous studies report an association between cognitive development and malnutrition (Powell et al., 1995; Pollitt et al., 1997), iron deficiency (Lozoff et al., 2000; Grantham-McGregor and Ani, 2001; Stoltzfus et al., 2001) and,
<br>20<br>20<br>15<br>10<br>10<br>5 0<br>0 -10<br>AFR China EAP LCR MNA ECA Developed Europe Other Developed<br>%, year-on-year %, year-on-year<br>Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-06 May-06 Sep-06 Jan-07 May-07 Sep-07 Jan-08 May-08 Sep-08 Jan-09 May-09 Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11<br><!-- End of picture text --> Source: IMF, International Financial Statistics.

Note: AFR: Africa; EAP: East Asia and Pacific; LCR: Latin America and the Caribbean; MNA: Middle East and North Africa; ECA: Emerging Europe and Central Asia.

# **2. Regulators around the world responded to the crisis by launching extensive reforms to create more resilient financial systems. Banks responded by repairing their balance**

> 1 For an in-depth analysis of the global financial crisis, see, for example, Claessens, Dell‘Ariccia,  Igan, and Laeven (2010), Brunnermeier (2009), Diamond, Douglas, and Rajan (2009), and Adrias and Shin (2008).
5.6 Projected Job Losses by Educational Attainment For both Jordan and Georgia, our methodology produces a pronounced educational gradient, with projected job loss rates declining in the level of education.

# **5.6.1 Jordan**

For Jordan, the educational gradient of job losses is similarly steep as that observed in the CMMHH. Job losses for “PFPS” workers with at most a basic education were on the order of 30 percent, whereas for those with higher

|higher<br>education<br>they were<br>around 17-<br>19 percent<br>(Table|_Table 5-21 Jordan: Distributio_<br>_Attainment_<br>By education|_n of PFPS Job Losse_<br>CFUWBES1<br>Modeled|_s (Percentage o_<br>CFUWBES2<br>Model-<br>predicted|_f Jobs Lost) by E_<br>CFUWBES2<br>Survey<br>Measured|_ducational_<br>CMMHH<br>Survey<br>Measured|
|---|---|---|---|---|---|
|5-21),<br>|Less than basic|19.3|29.9|28.0|33.9|
|relative to|Basic Education|16.2|28.3|25.0|29.9|
|our|Secondary Education<br>|15.1|24.9|25.8|21.4|
|model’s|Higher education<br>|13.3|17.4|24.2|19.4|
|<br>|Total|15.8|25.2|25.5|23.3|
|prediction|Sources: CFUWBES1, LMPS 2016,|and CMMHH Jordan 202|1.|||
|of 29.9||||||

and 17
have often been captured by country fixed effects. However, this cannot be applied to a crosssection analysis such as ours. Furthermore, many other characteristics of a country not necessarily related to sunk costs may be captured by the fixed effects.

In our study, we interpret sunk costs as the costs associated with entering the market and starting up a business. We include two separate measures of these costs. Our first measure controls for the minimum capital requirements banks need to comply with to begin operations in the countries in our sample. These data comes from a worldwide survey of bank regulators summarized by Barth, Caprio, and Levine (2001). We use data on the actual monetary amount of capital (expressed in U.S. dollars) banks have to put down and we also take into account the regulations in each country as to what may constitute bank capital. In particular, we build an index of initial capital stringency that can take values from 0 to 3, with higher number indicating greater stringency. This index comes from summing up responses to the questions below in the following way:

- (1) Can initial and subsequent infusions of regulatory capital include assets other than cash or government securities? 1 if no.

- (2) Can the initial infusion of capital be based on borrowed funds? 1 if no.

- (3) Are the sources of funds that count as regulatory capital verified by the

regulatory or supervisory authorities? 1 if yes.

Finally, we combine the dollar measure with the index of initial capital stringency by obtaining the principal component of the two series. We refer to the first principal component of the two series as _Minimum capital requirements._<sup>_8_</sup>

> 8 It should be noted that cross-border lending also requires capital for the head office and that it will generally be higher for more vulnerable host countries, for the same loan size. While the two needs (the minimum capital requirement to operate locally and the capital needed for cross-border lending) are
where >0 if<sup>and 0 otherwise and the error terms,andare assumed to</sup> follow a trivariate normal distribution. For purposes of comparability Appendix D presents univariate probit models which implicitly assume there is no correlation between the error terms. The dependent variables and are dummies indicating whether the individual in question engages in non-farm enterprise activity, works on the household farm or has worked for a wage, respectively. Note that these categories are not mutually exclusive.

The explanatory variables affect individuals’ relative returns and ability to participate in different activities; is a vector of individual characteristics, notably age, education, marital status and relation to the head of the household. is a vector of household characteristics, including the size and composition of the household; is a vector of investment climate proxies comprising prevailing local wage rates, distance to the nearest market and dummies indicating whether or not the community the household lives in is a rural town, is electrified, and whether a credit institution is present in the village. These objective proxies correspond to the key subjective investment climate constraints identified by managers of non-farm enterprises (see section 3(b) and Appendix C).<sup>13</sup> Finally, is a vector of dummies indicating partner’s education and the main occupation of the household head’s father. Information on the latter is not available in the Ethiopian dataset.

# **(c) Results**

Results are presented in Tables 3A and 3B. While the estimated coefficients do not necessarily imply causation, they nonetheless shed light on the drivers of gender differences in activity choice. That the decision to work in a non-farm firm cannot be analyzed independently of the decision to work for a wage or on the family farm is evidenced by the strong correlations between the error terms of the different equations. These correlations suggest that working on the family farm and wage work, and wage work and working for a non-farm enterprise are substitute 12
# **Learning to Navigate a New Financial Technology: Evidence from Payroll Accounts**<sup>_∗_</sup>

Emily Breza<sup>_†_</sup> Martin Kanz<sup>_‡_</sup> Leora Klapper<sup>_§_</sup>

**Keywords** : Learning, financial inclusion, financial consumer protection. **JEL classification: G5, G51, G53, O16**

> _∗_ We are grateful to Pierre Bachas, Shawn Cole, Xavier Gin ́e, Jessica Goldberg, Rawley Heimer, Supreet Kaur, Michael Kremer, Rocco Machiavello, David McKenzie, Simone Schaner, Lore Vandewalle and numerous seminar and conference participants for helpful comments and suggestions. Financial support from the Asian Development Bank, Innovations for Poverty Action Global Financial Inclusion Initiative, International Growth Centre, U.S. Department of Labor, and the World Bank is gratefully acknowledged. This study was approved by the Harvard and Columbia University Institutional Review Boards and registered in the American Economic Association Registry for randomized control trials (AEARCTR-0001107). Camilla Fabbri, Maura Farrell, Rayhanul Islam, Michel Nehme, Smita Nimilita, Minita Varghese, and Rebecca Wu provided outstanding research assistance. The opinions expressed in this paper do not necessarily reflect the views of the World Bank, its executive directors or the countries they represent.

> _†_ Harvard University and NBER, Email: `ebreza@harvard.edu` .

> _‡_ World Bank and CEPR, Email: `mkanz@worldbank.org` .

> _§_ World Bank, Email: `lklapper@worldbank.org` .
Economic Development: Evidence from China." _Journal of Development Economics 78_ (1): 241-70.

Deininger, K. and S. Jin. 2008. "Land Sales and Rental Markets in Transition: Evidence from Rural Vietnam." _Oxford Bulletin of Economics and Statistics 70_ (1): 67-101.

Fenoaltea, S. 1976. "Risk, Transaction Costs, and the Organization of Medieval Agriculture." _Explorations in Economic History 13_ (2): 129-51.

Foster, A. and M. R. Rosenzweig. 2010. "Barriers to farm profitability in India: Mechanization, scale, and credit markets." . Berkeley, CA: Paper presented at the Conference on the Rural Economy, Sept. 24, 2010.

Frangakis, N., M. Salamun, and E. Gemi. 2008. "Property restitution in Albania." . Brussels: Eurpean Parliament, Committee for Foreign Affairs.

Frisvold, G. B. 1994. "Does Supervision Matter? Some Hypothesis Tests using Indian Farm-Level Data." _Journal of Development Economics 43_ (2): 217-38.

Heston, A. and D. Kumar. 1983. "The Persistence of Land Fragmentation in Peasant Agriculture: An Analysis of South Asian Cases." _Explorations in Economic History 20_ (2): 199-220.

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Lin, J. Y. 1992. "Rural Reforms and Agricultural Growth in China." _American Economic Review 82_ 34-51.

27
**F. CONCLUSIONS**

In this paper we have studied the determinants of market integration and price transmission for five major commodities in Indonesia. In a context where commodity prices have been changing dramatically, it is particularly relevant for natural resource abundant countries, like Indonesia, to understand what drives the transmission of price signals. On the one hand, this will allow the government to take appropriate measures to facilitate price transmission across regions so that producers can take optimal production decisions. On the other hand, it will allow the government to better target its policies geographically to mitigate the impact of a particular price shock on the poor population. Using detailed price data covering 25 Indonesian provinces for more than a decade, we shed some light on the drivers of the transmission of price signals and our findings can be summarized as follows.

First, we found that the degree of market integration varies across different commodities. Rice and sugar markets are highly spatially integrated, while cooking oil, soybeans and maize markets much less so. This is consistent with smaller price differences across provinces in the former markets than in the latter.

Second, when focusing on the determinants of these price differences among provinces we obtained some consistent findings irrespectively of the commodity analyzed. Remoteness and the interaction between remoteness and quality of infrastructure clearly influence price differentials. Remote provinces pay a higher price, but the effect of remoteness is attenuated by good transport infrastructure. Thus, there is no geographical determinism for remote provinces to pay much higher prices. Furthermore, price differences are also significantly explained by output per capita and land productivity.

Third, unlike in the case of the previous variables, the effect of income per capita on price differentials varies across commodities. We argue that income per capita is at the same time
only in two, out of twenty-one, developing countries there is evidence of labor market de-routinization. Aedo et al. (2013) estimate trends for 30 countries at different stages of development and find that the share of jobs intensive in non-routine, cognitive tasks is higher in richer countries.

To our knowledge, there are only three studies that use data on the task content of occupations from developing countries instead of relying on data from the US. Dicarlo et al. (2016) use data from STEP surveys to determine if the skill content of jobs is different from that suggested by USbased skill surveys. Messina, Pica, and Oviedo (2014) analyze trends in the task content of jobs in four Latin American countries but do not investigate the drivers of such trends. Finally, Hardy et al. (2018) investigate the task content of jobs using country-specific skills surveys for 46 economies, mostly in the developed world. They analyze if the findings are different from those obtained when using US data from O*NET and investigate the drivers of the heterogeneity in the 4
|1.66|0.90|0.21|

Note: Calculations are based on data from the IMF Direction of Trade Statistics While the South Asian countries demonstrate TIIs (not normalized) greater than unity, some relatively increasing and some decreasing as seen from Table 3, when normalized for geographic proximity, their relative importance to each other becomes less pronounced. This is illustrates from statistics in Table 4 which reports the ratio of observed to predicted TIIs for each of the countries, thus demonstrating the magnitude of the difference in actual (not normalized) TIIs and the TII that would be predicted given the geographical distance (i.e. at a given point along the regression line). For example, India’s “real” trade intensities, normalized for geographical proximity, with the other South Asian countries are stronger than that implied by its bilateral trade shares. In 1998, India traded more than 3 times as much as should have been expected with Sri Lanka, more than 51⁄2 times as much as should have been expected with Bangladesh, and 8 times as much as should have been expected with Nepal.

However, for the peripheral countries, trade with the region is below what should have been expected given the size of the partners’ import market and their geographical proximity. For 13
with strong e-commerce legislation are much more likely to promote sharing of both public and private intent data. Of some concern is the finding that governments with a well-established regulatory framework to facilitate e-commerce/transactions, appear not to be adopting adequate safeguards to protect personal data. Take Indonesia as an example, where Law No. 11 of 2008 on Electronic Information and Transactions (and amendments to the law) establishes an enabling environment for e-commerce and e-transactions by granting the equivalence of paper-based and electronic communications, recognizing e-signatures, and adopting the technological neutrality principle. But there is no general law protecting data, despite scattered, sector-specific regulations.

**Table 4. Regulatory good practices, by country income across dimensions (in percentages)**

|||_Safeguards_<br>|<br>||_Enab_<br>|_lers_<br>||
|---|---|---|---|---|---|---|---|
|**Country income**<br>**level**|**Personal**<br>**data**|**Non-personal**<br>**data**|**Cybersecurity**<br>**and**<br>**Cybercrime**|**Cross**<br>**border data**<br>**flows**|**E-commerce**<br>**/transactions**|**Public**<br>**intent data**|**Private**<br>**intent data**|
|High income|59|43|73|42|86|69|30|
|Upper middle income|46|29|57|30|74|62|20|
|Lower middle<br>|43|38|55|24|72|44