rafmacalaba/data-use-mentions-extended huggingface dataset:
90 percent of respondents said it had helped build trust between citizens and authorities (Masud 2015).

# **Interoperability**

When data sources are interoperable the potential use and value of data expand. As one example, interoperability can help governments prioritize scarce resources by cutting costs such as by eliminating duplicate or ghost recipients of social transfers—beneficiaries, often of pension funds, who are no longer alive. This was the case in Argentina where the government identified noneligible beneficiaries across various social programs using the country’s system of unique taxpayer ID numbers to link data sets, generating an estimated savings of $143 million over eight years (World Bank 2020a and World Bank 2018b).

Interoperability can also help improve targeting. In Jordan, the Profile Global Registration System (ProGres), the main database used by the UNHCR to register refugees, has traditionally been used to target cash assistance to refugees outside camps. This database contains key socioeconomic characteristics on almost all refugees since most register with UNCHR at some point in time. ProGres does not contain information on expenditure of households, though, preventing transfers to be targeted explicitly to poor households. Combining ProGres with the Jordanian Home Visits database, which contains only a subset of the population of interest, but 19
casualties in columns 3 and 4 of Table 9, with the magnitudes very similar to the baseline results in Table 8. This suggests that although nationalist sentiment associated with Japanese invasion may help to explain the CCP’s mobilization ability, it alone could not fully explain peasants’ support of the communist military forces.

**6.6. Famines, Land Ownership, and Communism**

Land has been the key asset in agrarian societies, and inequality in land ownership has always been a key trigger of violence and rebellion in ancient times (Scheidel 2017). In the Late Roman Republic, Tiberius Gracchus gained popular support by proposing land reforms which attempted to “stem the numerical decline of the free peasantry from which the armies of the Republic were traditionally recruited” (De Ligt and Northwood, 2008, page 1). His assassination marked the beginning of a long period of chaos which ended the Republic. Likewise, the CCP’s advocation for redistribution of land served as a powerful catalyst to recruit peasants with little or no land (Hsiao 1969). Indeed, ever since its military insurrection, the CCP was keen to carry out land reforms to attract the support of peasants in areas under its control by redistributing land from landowners to peasants, and would later call its revolution the Land Revolution. Fully aware that their distributed land would be gone if the CPP failed, a large number of peasants joined the CCP army, providing the CPP with a large fighting force.

To further our understanding of the underlying mechanism of our previous findings, we collect land ownership data from the Agri-business Statistics of the Republic of China (19181921). This data set was compiled from land surveys in 152 prefectures conducted by the then Ministry of Agri-business. Because of the unstable political environment during the warlord period, some prefectures dropped out of the survey, resulting in an unbalanced panel. The data set contains information on the fraction of rural households being tenants, that is, being landless, which is a reasonable measure of rural inequality
IHS 2012-2016 and HIES 2017. _Notes:_ Deciles are created based on per capita household consumption.

# (B) Reduced Medical Expenses

<u>Figure 4</u> plots the effects of reducing out-of-pocket medical expenses after the tax-induced price increase in cigarettes. This mechanism yields positive income gains for all deciles, as the price shock encourages a reduction in smoking, and hence, a drop of tobacco-related medical expenses. Households can then benefit from higher disposable income, as they are no longer burdened by those medical bills. Moreover, the combination of high smoking prevalence and high sensitivity (elasticity) to price changes among lowerincome households results in a progressive relative incidence. The magnitudes of these benefits are large for the lower-income deciles. Assuming a price shock of 50 percent, on average, the long-term reduction 18
# **4 Results**

## **4.1 Descriptive Results**

## **4.1.1 Baseline Balance**

Table 1 provides summary statistics of covariate balance at baseline by treatment arm. The respondents in my sample are on average 42 years old. They have 1.9 children on average; 63 percent are married. About one-third of the sample lives in a rural area. Small imbalances are observed with respect to educational attainment. One in four respondents has completed higher education; the percentage is 29 percent in T1 and 23 percent in T2 and control. I control for educational attainment in all regressions and later introduce survey weights as a robustness check.

On average, 42 percent of my sample work full time and 21 percent are homemakers. The Online Appendix compares my sample’s geographical spread by governorate to the latest national statistics (Institut National de la Statistique; 2022a) and shows that it is highly geographically representative. It also compares the share of age cohorts (Institut National de la Statistique; 2022b): younger cohorts are overrepresented in my sample. In the robustness checks, I show that weighing down younger cohorts does not affect my conclusions.

## **4.1.2 Second-Order Beliefs**

The belief that a majority of one’s peers opposes political change may affect individual opinion and behavior. For example, an individual may refrain from taking a particular action or expressing a political opinion because she thinks that society will disapprove of or sanction it. Generalized misperceptions about public opinion may therefore hinder social change and keep society captured in inefficient social equilibria (see Kuran, 1991). When studying socially contentious issues such as the reform of law of religious origin, paying attention to individuals’ perceptions of what is the public opinion - second-order beliefs - is thus crucial.<sup>12</sup> Figures 3, 4, 5 and 6 aggregate respondents’ second-order beliefs about their

> 12I use the term “first-order belief”
0.565***|803|0.251|0.347***|964|
|<br>Started or increased delivery of goods, services<br>or carryout|0.366|0.567***|803|0.234|0.329***|965|
|Started or increased remote work|0.472|0.538|801|0.433|0.509***|988|
|Exporting directly (at least 10% of sales)|0.052|0.107|734|0.093|0.073|878|

Note: This table shows descriptive statistics from El Salvador and Georgia COV-ES data. Round 1 and Round 2 columns show the respective statistics of firms in the respective round. N denotes the number of observations. Means are weighted using the sampling weights from the ES. Asterisks reported in the columns for Round-2 are for unconditional t-tests comparing means across the two rounds. For these t-tests, standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

36
trade and high technology trade. Second, it addresses the problem of zero trade flows between countries by adopting a bivariate distributed probit regression model. Second, to measure the strength of IPRs regimes, we make use of a fine tuned index on national IPRs systems developed by Park and Ginarte (1996). Our results confirm previous findings suggesting a positive link between IPRs protection and trade flows for the total non-fuel trade aggregate. However, IPRs are not found to be significant for high technology trade flows.

> ' These estimates are based on trade data from the UN Comtrade database. For the definition of high technology products, see Table 1.

> 2 For a detailed review of the TRIPs Agreement and its economic implications, see Primo Braga (1996).

1
We may calculate the left hand side of equation 5, since the three values on the right hand side are known from equations 1, 2, and 3. Equation 5 implies that even if initial wages differ between the CGE and micro models, the percentage change in the wage gaps will be consistent across the two models. By passing on percentage changes in wage premia by type of worker, instead of percentage changes in wages, the possibility of wage gaps moving in opposite directions in the macro and household data is eliminated. Within each group of workers, distributional changes occur; but, on average, for any group of workers, the relative wages for each type of worker are constrained to be consistent with the corresponding growth rates from the CGE model.

Given the known values in equations 1-5, and defining average wages for unskilled labor in agriculture as numeraire in the GIDD, so that y′�,� �y�′�,�, it is possible to calculate the percentage changes in average wage income of households in sector _s_ and skill level _e_ that are consistent with wage gaps expressed in Equation 5: Note that Equation 6 only operates on labor income. In order to adjust the micro data such that the weighted average percentage change in the per capita income/consumption across all households matches the change in real consumption per capita in the CGE model, a subsequent adjustment is carried out. Define Y as real per capita income calculated from the CGE model in the benchmark and Y<sup>�</sup> as its predicted value in the CGE model simulation. Define γ�� �∑�∈� y�,�� /n� as the per capita income of household h in the benchmark equilibrium, where y�,�� is the income of the ith member of household _h_ , and n is equal to the size of household _h_ ; . similarly, define λγ��� �∑�∈� λy��,�� /n�where
these two mechanisms mean that an exogenous X% increase in total employment also increases long run GDP per capita by X%. A similar capital-adjustment mechanism is at the heart of economic interpretation of the World Bank Human Capital Index (Kraay 2018) and the Utilization-adjusted Human Capital Index (Pennings 2020).

While the GEGIs do not capture all of the mechanisms through which gender employment gaps affect growth, they are highly correlated with more comprehensive measures in other studies (>95% across regions). This suggests GEGIs capture some of the most quantitatively important channels. Other mechanisms the GEGIs do not capture include effects of female employment via fertility (Galor and Weil 1996), effects through faster growth due

> 2 This is a consequence of the well-known U-shaped relationship of female labor force participation with income first documented by Goldin (1995). However, Klasen (2019) argues that the U-shaped pattern is mostly due to region fixed effects, and individual countries do not display this pattern along their development path.

2
(Dormann et al. 2013). We use two ML algorithms Conditional inference forests (CIF) and Least absolute shrinkage and selection operator (LASSO).

We find that ML methods outperform standard OLS in out-of-sample prediction by 14-24 percent. Also, the identified variables of importance are largely consistent across our two, very different, ML methods. ML results are in line with previous research in that commonly used teacher characteristics such as, teacher gender, education qualifications, experience etc. do not seem to hold much predictive power for student learning gains. Instead, what teachers do in terms of specific classroom practices (measured through classroom observations and student surveys) and what teachers believe in terms of how they perceive the abilities of their students and the environment around them (measured through teacher surveys) are consistently revealed to be important.

Overall, teacher covariates matter more, and differently, for Math than Kiswahili. For Math, the teacher belief that they can help disadvantaged and struggling students learn; the teacher practice of providing clear and helpful written feedback (on homework and tests); and the teacher preparation in teaching foundational concepts are the three most predictive factors for student learning gains. For Kiswahili, where teacher (and other observable) covariates are on the whole less predictive, teacher preparation, practice, and beliefs still emerge as being important. Specifically, the teacher belief that action is taken against poor teacher performance; the teacher practice of providing extra help to struggling students<sup>4</sup> ; and (as in Math) teacher preparation in teaching foundational concepts are the three most predictive teacher covariates for student learning gains.

Our paper contributes in two ways to the literature on teacher effectiveness. It demonstrates how machine learning techniques can help address long-standing prediction problems in education economics, including that of predicting student learning gains linked to a teacher. Applications of ML in education economics are still not very common. We were able to find three types– one for predicting student dropouts (Aulck et al. 2016
point to a very large number, on the order of magnitude of US$1 trillion annually.<sup>3</sup> Furthermore, these lessons are also likely to be applicable to non-transport infrastructure investments such as electrification or Internet connectivity programs.

We study a rural roads improvement project in Vietnam using mixed methods analysis to assess whether the impacts of road rehabilitation differed across genders, and to better understand the underlying mechanisms. Specifically, we first measure causal impacts of road improvement on average and by gender applying a difference-in-differences framework to a combination of survey and administrative data. We then complement these results with qualitative data collection that aims to shed light on the mechanisms that may prevent either gender from fully benefiting from infrastructure improvements economically and socially.

The remainder of the paper is structured as follows: we describe the project interventions in more detail in Section 2. Sections 3 and 4 discuss the data and methodology, respectively. Results are discussed in Section 5. Section 6 provides conclusions.

# 2. Intervention Description

We analyzed rural road improvements carried out under a large-scale project, the Third Rural Transport Project (RTP3), in Vietnam<sup>4</sup> ( <mark>World Bank, 2015) t</mark> hat was implemented between 2008 and 2015 (Table A.1). Under the project, approximately 3,100 km of rural roads were rehabilitated, and maintenance activities were implemented on over 19,000 km of rural roads spread across 33 provinces in Northern and Central Vietnam (Figure A.1).

The interventions under RTP3 cover activities classified as either rehabilitation or maintenance under the program and in the administrative data that we use for the analysis. Road maintenance is important since it receives a low level of investment despite being critical to sustaining road quality in the face of not only traffic impacts, but also severe weather.<sup>5</sup> Flooding, landslides, and slope and coastal erosion during the rainy season are common occurrences in many rural
in addition, an investment slowdown and increase in imports also impacted growth during the three quarter deceleration period of Q3, 2016–17 through Q1, 2017–18.<sup>33</sup> Second, many economic indicators now firmly point that these events had a transient impact as the economy has been slowly recovering from them.<sup>34</sup> Growth has since steadily accelerated to 6.3, 7.0, and 7.7 percent in the last three quarters spanning Q2, 2017–18 to Q4, 2017-18. Economic revival is also evident in high frequency indicators such as the Purchasing Manager Index (PMI) and the Index of Industrial Production (IIP). Both of these confirmed a sharp slowdown in the months surrounding the introduction of the GST, but have recovered and have registered a consistent expansion in recent months. While consumption and services continue to be the main drivers of growth in the last three quarters of 2017-18, the contribution of the public sector to GDP growth has declined, and growth in the manufacturing and construction sectors has accelerated. Even as the investment rate broadly remains burdened by stressed balance sheets of banks and corporates (twin balance sheet issues), investment growth has picked up in recent quarters. Credit growth, and to a lesser extent exports growth, has also recovered in recent months after a protracted period of deceleration (Figure 12).<sup>35</sup>

**Figure 12: High frequency data suggests some uptick in credit growth and exports**

<!-- Start of picture text --> 20 30<br>18<br>20<br>16<br>14<br>10<br>12<br>10 0<br>8<br>-10<br>6<br>4 -20<br>2<br>0 -30<br>Credit Growth: SCBs 3mma Export Growth 3mma<br>Apr-13 Aug-13 Dec-13 Apr-14
Another relevant question is whether the credit ratio should take into account other denominators besides GDP, such as financial assets or total assets of the private sector. Although GDP is correlated to a significant extent with private sector income and therefore serves as an indicator of the ability to repay a given amount of loans, holdings of financial assets (deposits and securities investments) and nonfinancial assets (for example, real estate) are also relevant to the evaluation of excessive credit.

Chart 18 presents credit gaps with alternative denominators (GDP and financial assets and total assets of the private sector) calculated using the HP filter on data for bank loans in the Czech Republic with a high lambda (400,000) as proposed in Basel III. The filter is applied to quarterly data for the period 1998–2010, which, however, is regarded as relatively short from the international perspective (Basel III recommends at least a 20-year period). The estimates indicate that the current level of bank loans is below the long-term trend. However, the trend estimate is subject to a range of problems related to the short time series—above all to extraordinary factors linked with a fall in credit volume in 1998–2002, which was caused by a banking crisis in the 1990s, and the cleanup of bank balance sheets ahead of the privatization of large banks.

**Chart 18. Credit gaps in the Czech Republic Chart 19. Credit-to-GDP ratios for a similar with alternative denominators (in %) level of economic development (in %; GDP per capita in 2005 USD = USD 17,000 approx.)**

<!-- Start of picture text --> 100<br>20 90<br>15 80<br>70<br>10<br>60<br>5 50<br>0 40<br>30<br>-5<br>20<br>-10 10<br>-15 0<br>03/98 03/00 03/
(0.0711)|||0.0747<br>(0.0785)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.625|0.625|0.625|0.613|0.613|0.613|

_Note:_ The table presents the results of firm-level regressions of employment on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

17
Resettlement arrivals<br>Births<br>Administrative corrections|<br><br><br><br><br>|Repatriation<br>Resettlement<br>Cessation<br>Naturalization<br>Deaths<br>Administrative corrections|
|**IDPs**|<br><br>|New internal displacement<br>Births<br>Administrative corrections|<br><br><br><br>|Cross border flight, becoming an<br>asylum-seeker or refugee<br>Return<br>Settlement elsewhere in the country<br>Local integration<br>Administrative corrections|

_Source_ : UNHCR Global Trends, IDMC Forced Displacement Data Model
approach for targeting since the survey data would be part of the regular MPCA targeting program. The CLCI adopted a census-like door-to-door house-visit approach in selected neighborhoods chosen based on reported high levels of need. Data collection was non-random, reflecting only households in neighborhoods already identified as vulnerable. The total sample size was 10,818 households. Summary statistics of the variables of interest are shown in Table 1. By design, the data comes from the North – CLCI’s area of intervention at the time – and, thus, is not nationally representative of the displaced population in Iraq. It is, however, representative of the caseload of the CLCI’s assistance and the lessons shared in the paper are highly relevant in the conflict-affected contexts with significant displaced populations and the presence of humanitarian cash transfer programs.

_Table 1: Summary statistics of variables of interest (n = 10,818)._

|Household characteristics|Frequency|%|
|---|---|---|
|Female-headed household|3042|28|
|The household head is disabled|1559|14|
|No household member worked|3745|35|
|The household cannot afford basic need|6680|62|
|The household head document is missing|1636|15|
|Rural households|5472|51|
|Lowest asset quantile|2792|26|
|Status|||
|Displaced_(& refugee)_|1388|13|
|Host_(& remainee)_|639|6|
|Returnee|8791|81|
|Governorate|||
|Anbar|2334|22|
|Diyala|180|2|
|Kirkuk|477|4|
|Ninawa|6113|57|
|Salah_al_din|1714|16|
|Referral|||
|Registered for government social assistance|7078|65|
|Registered for Minha COVID 19 program|3297|30|
|Willingto be referred|7278|67|

# 3.3 Analytical strategy

The analytical strategy adopted
(2022), Financial Cycles in Euro Area Economies: A Cross-Country Perspective Using Wavelet Analysis. Oxford Bulletin of Economics and Statistics, Vol. 84, No. 3, pp. 569-93.

Melolinna, M., (2015), “What Has Driven Inflation Dynamics in the Euro Area, the United Kingdom and the United States?”, ECB Working Paper 1802, European Central Bank.

Miranda-Agrippino, S., and Rey, H., (2020), “The global financial cycle after Lehman”, AEA Papers and Proceedings, Vol. 110, pp. 523–28.

Monacelli, T., and Sala, L., (2009), “The International Dimension of Inflation: Evidence from Disaggregated Consumer Price Data”, Journal of Money, Credit, and Banking, Vol. 41 (s1), pp. 101-20.

Mumtaz, H., and Surico, P., (2009). “The Transmission of International Shocks: A Factor-Augmented VAR Approach”, Journal of Money, Credit and Banking, Vol. 41 (s1), pp. 71-100.

Orphanides, A., and Norden, S. V., (2002), “The unreliability of output-gap estimates in real time,” Review of Economics and Statistics, Vol. 84, No. 4, pp. 569-83.

Orphanides, A., (2003), “Historical monetary policy analysis and the Taylor rule”, Journal of Monetary Economics, Vol. 50 No. 3, pp. 983-1022.

75
1999) find what they term „the market stealing effect‟ of foreign direct investment which swamps the positive effect of technology transfer on firm productivity in Venezuela. Javorcik (2004) explicitly distinguishes between intra- and inter-industry effects of foreign direct investment using firm level data from Lithuania and finds that foreign direct investment has a positive productivity effect on supplier industries but no significant effect on local competitors in the same industry. Javorcik and Li (2008) show that entry of foreign retail chains boosts the productivity of the supplying industries in Romania.

6
that countries starting out poorer experienced higher relative rates of poverty reduction thereafter. This counterintuitive result is attributed to initial poverty, which, as revealed by a decomposition of the speed of poverty convergence, offsets the advantage of higher growth rates among poorer countries, that is, income convergence and the growth elasticity of poverty reduction.

Taking advantage of a unique panel dataset (1992–2014) on income, poverty, and inequality across municipalities in Mexico, this paper tests most of the above conclusions to provide a more disaggregated, longer-term perspective on the convergence paths and changes in well-being. Previous studies of convergence in Mexico have mainly focused on income growth paths among states, and, while some have found evidence of convergence in the years before the end of the import substitution model, most have consistently reported evidence of divergence after that. For 5