rafmacalaba/datause-encoder-data huggingface dataset:
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Damania, R., Desbureaux, S., Rodella, A. S., Russ, J. & Zaveri, E. (2019). _Quality unknown: the invisible water crisis_ . World Bank Publications.

Dash, J. (2019). Satellites and crop interventions. _Nature Sustainability_ , _2_ (10), 903-904.

De Groote, Hugo, Masresha Tessema, Samuel Gameda, and Nilupa S. Gunaratna. "Soil zinc, serum zinc, and the potential for agronomic biofortification to reduce human zinc deficiency in Ethiopia." _Scientific Reports_ 11, no. 1 (2021): 1-11.

Ding H., A. Markandya, R. BARBIERI, M. Calmon, M. Cervera, M. Duraisami, R. Singh, J. Warman, and W. Anderson. 2021. Repurposing Agricultural Subsidies to Restore Degraded Farmland and Grow Rural Prosperity. Washington DC, World Resources Institute.

Duflo, E., Kremer, M., & Robinson, J. (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. _American economic review_ , _101_ (6), 2350-90.

Erisman, J. W., M. A. Sutton, J. Galloway, Z. Klimont, and W. Winiwarter. 2008. “How a Century of Ammonia Synthesis Changed the World.” _Nature Geoscience_ 1 (10): 636–39.

Fishman, R., Kishore, A., Rothler, Y., Ward, P., Jha, S., & Singh, R. (2016). Can information help reduce imbalanced application of fertilizers in India. _Experimental evidence from Bihar. International Food Policy Research Institute (IFPRI) Discussion Paper_ , _1517_ .

Gautam, M. (2015). Agricultural subsidies: Resurging interest in a perennial debate. _Indian Journal of Agricultural Economics_ , _70_ (902-2016-68359), 83-105.

_Gautam, Madhur; Laborde, David; Mamun, Abdullah; Martin, Will; Pineiro, Valeria; Vos, Rob. 2022. Repurposing Agricultural Policies and Support : Options to Transform Agriculture and Food Systems to Better Serve the Health of People, Economies, and the Planet. World Bank, Washington, DC_ Giné, X., Patel, S., Ribeiro, B., & Valley, I. (2019). Efficiency and equity of input subsidies: Experimental evidence from Tanzania. _American Journal of Agricultural Economics_ .

Gram, G., Roobroeck, D., Pypers, P., Six, J., Merckx, R., & Vanlauwe, B. (2020). Combining organic and mineral fertilizers as a climate-smart integrated soil fertility management practice in sub-Saharan Africa: A meta-analysis. _PloS one_ , _15_ (9), e0239552.

_Government of India. 2016. Economic Survey 2015-16, Volume I, Department of Economic Affairs, Economic Division, Ministry of Finance_ _Goyal, Aparajita; Nash, John. 2017. Reaping Richer Returns : Public Spending Priorities for African Agriculture Productivity Growth. Africa Development Forum;. Washington, DC: World Bank and Agence Francaise de Developpement. © World Bank._ Gulati, A., and P. Banerjee. 2015. “Rationalizing Fertilizer Subsidy in India: Key Issues and Policy Options.” Working Paper 307, Indian Council for Research on International Economic Relations. - <u>https://www.academia.edu/14970315/Rationalizing_Fertilizer_Subsidy_in_India Key_Issues_and_Policy_Options.</u> 18
|||||(0.002)|(0.002)||||(0.003)|(0.003)|
|---|---|---|---|---|---|---|---|---|---|---|
|Population (logs)|||||0.028|||||0.090|
||||||(0.056)|||||(0.065)|
|Primary Education|||||-0.009*|||||0.006|
||||||(0.005)|||||(0.010)|
|Freedom from Business|||||0.035|||||0.008|
|Regulations|||||(0.098)|||||(0.099)|
|Merchandize Trade to GDP|||||-0.001|||||-0.004|
|ratio|||||(0.003)|||||(0.003)|
|Growth rate of GDP per|||||0.018|||||0.054***|
|capita (%, annual)|||||(0.023)|||||(0.014)|
|Inflation (GDP deflator)|||||0.016|||||0.016|
||||||(0.013)|||||(0.015)|
|Rule of Law|||||-0.115|||||0.346|
||||||(0.266)|||||(0.336)|
|Control of Corruption|||||0.171|||||-0.149|
||||||(0.284)|||||(0.317)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|11.170***|5.262***|4.965***|4.838***|5.240***|10.615***|3.226**|2.394*|2.344*|0.954|
||(0.488)|(1.092)|(1.027)|(0.999)|(1.945)|(0.577)|(1.417)|(1.382)|(1.369)|(2.994)|
|Number of observations|19,485|19,485|19,485|19,485|19,485|6,056|6,056|6,056|6,056|6,056|
|R-squared|0.265|0.322|0.338|0.347|0.355|0.198|0.265|0.287|0.302|0.322|

Huber-White robust standard errors clustered on country in brackets. Significance is denoted by *** (1%), ** (5%), * (10%) 49
**Table 4. Selected Offshore Services Providers’ Hiring and Lay Off Actions during the** **<u>Economic Crisis</u>**

|**Company**|**When**|**Laid off**|**Hiring**|**What**|**Where**|
|---|---|---|---|---|---|
||Mar 2009|500||Crisis effect|Manila, Philippines|
||Aug2009||100|Re-hiring part of thepeople laid off|Philippines|
|**Accenture**<br>United States|Aug2009|300||Crisis effect and restructuring|Worldwide|
|<br>(Ranking: 2)|Oct 2009||NA|Acquisition to serve new sector|US|
||Feb 2010||NA|Acquisition|Rio de Janeiro, Brazil|
||2010||8000||Mostlyin India|
||Late 2008||20||India|
|**CSC**|Nov 2008||5|Acquisition to serve local customers|Tianjin, China|
|<br>United States<br>(Ranking: 4)|Early<br>2009|98|||East Greenbush, US|
||Aug 2009||550|Acquisition to serve local customers<br>and strengthenexisting sector|Brazil|
||Sept 2009||65|New center to offer shared services|Albany, US|
||April<br>2009|56||Layoff of managers|Irving, US|
||May2009|100||Layoffs of middle management|Chennai, India|
|**Capgemini**<br>France<br>|Jun 2009||45|New center to perform IT Help Desk<br>Support andBusiness Continuity work|Iasi, Romania|
|(Ranking: 5)|Nov 2009|85||Layoffs of middle management|Dallas, US|
||Nov 2009||10|New center|Bangalore, India|
||Gen 2010||200|New BPO to serve local customers|Katowice, Poland|
|**Logica**<br>UK|Apr 2009||NA|New center on International Utilities<br>Competence Center|Portugal|
|(Ranking: 8)|Oct 2009||NA|New center on ITS-based solutions|Chennai, India|
||Nov 2009|2200||Laid off for restructuring|UK|
||Sept 2008|1000||Laid off for restructuring|India|
|**Wipro**|Dec 2008||2500|Acquisition to follow customer|India|
|<br>India|2009||1000|New center|Philippines|
|(Ranking: 11)|Q3 2009|630||||
||Nov 2009||100|New center, IT & BPO|Chengdu, China|
||Jan 2010|85||Layoffs in telecom R&D sector|Finland|
||2009||2400|New center|Guatemala, South Africa,<br>Philippines, Romania,<br>Morocco,India|
|**Genpact**<br>India|Mar 2009||500|New center to serve customers in EU|Lublin, Poland|
|(Ranking: 20)|Oct 2009||70|New ROC center|Hyderabad, India|
||2010||10,000||Worldwide|
||Feb 2010||1,200|Acquisition|India and US|

Note: Ranking refers to the top 20 offshore services companies (see Table 2) Source: CGGC based on company websites, company press releases, news articles and reports from consulting firms.

16
# **6. Descriptive Evidence**

The most basic analysis proceeds from a comparison between treatment and control districts of the average enrollments in various classes over the course of the program. These enrollment figures are plotted in Figures 4 through 7 for girls in classes 5 through 8 respectively. Of these classes 6 to 8 are eligible classes under the stipend program. The enrollment data on girls in class 5 serves as a placebo test in the absence of strategic choices by parents. Since the stipend is only for classes 6 to 8 there should be no effect of the program on enrollment in class 5. I plot the averages for treatment and control districts over time and along with their 95% confidence intervals. At baseline there are differences in each grade as would be expected. Over time the confidence intervals begin to overlap suggesting that there is a change in enrollments for the treatment districts. Figures 4 through 7 suggest that the program increased girls’ enrollment not only in classes 6 to 8 but also somewhat in class 5.

Similarly since the program is only for girls attending classes 6 to 8, there should be no change in boys’ enrollment. In the absence of spillover effects, the enrollment of boys in grades 6 to 8 serves as a placebo test. Figures 8 through 10 reveal that the enrollment of boys in both treatment and control groups follows a similar pattern for classes 5 to 7. For boys’ enrollment in class 8 (Figure 11) there is slight evidence of a spillover effect as the enrollment of boys in the treatment district appears to catch up to those in the control districts. Taken together the evidence in Figures 8 through 11 suggests some evidence of a spillover effect due to the stipend treatment.

Figures 12 and 13 present district level aggregates for the eligible classes and exploit the government’s use of the literacy rate cut-off to differentiate treated and untreated districts from each other. In figure 12 I order treated districts by the literacy rate (the so-called forcing variable 10
# Reserve Requirements in the Brave New Macroprudential World<sup>*</sup>

Tito Cordella,<sup>†</sup> Pablo Federico,<sup>‡</sup> Carlos Vegh<sup>§</sup> and Guillermo Vuletin<sup>**</sup>

**JEL Classification Numbers:** E44, E58, F31, F32, F38, G28

**Keywords:** Reserve Requirements, Macroprudential policies, Monetary Policy, Capital Flows, Exchange Rate, Business Cycle

* This working paper summarizes the main policy conclusions of a regional study sponsored by the Chief Economist Office of the Latin American Region of the World Bank. The study was conducted by a team composed by Tito Cordella, Augusto de la Torre, Pablo Federico, Alain Ize, Samuel Pienknagura, Carlos Vegh, and Guillermo Vuletin.  Excellent advice has been received from our peer reviewers Eva Gutierrez, Luis Jacome, and Andrew Powell. We would also like to thank participants at conferences/seminars at the Bank of Spain, Central Bank of Turkey, CEMLA, Central Bank of Uruguay, IDB, and IMF for very insightful comments and suggestions, and Gabriela Calderon Motta and James Trevino for excellent editorial assistance. Financial support from the Government of Spain through the SFLAC Trust Fund is gratefully acknowledged. The usual disclaimers apply.

- Development Economics (DECOS), The World Bank

- BlackRock

- § Johns Hopkins University and NBER

- ** The Brookings Institution
trends in wage inequality.

To develop our strategy, we set up unusual historical data sets of trends in trade reforms, trends in wages, and trends in wage inequality in Argentina. The data span the period 1974-2001. We construct a time series of tariffs, for different sectors in different years, and a time series of labor force surveys with data on individual wages. This is the first instance in this literature in which such a historical record of trade reforms is put together with a historical micro data set of workers and wages.<sup>2</sup> The outcome is almost 30 years of data on sectoral tariffs and individual wages that, we believe, provide a different, useful, and compelling identification strategy.

In addition to exploring the overall match between trends in tariff reforms and trends in wage inequality, we use these data to compare the two liberalization episodes of the 1970s and 1990s and to examine some key differences between them. This is a contribution to the literature on trade liberalization in Latin America, which has focused almost entirely on the 1990s. However, in Argentina, both the increase in wage inequality and the trade reforms were much more marked in the 1970s than in the 1990s. The skill premium, for instance, doubled in the 1970s but increased by 42 percent in the 1990s. In addition, the average tariff reduction of the 1970s was of nearly 70 percentage points (from 100 to 30 percent) but was of 12 percentage points (from 30 to 18 percent) during the 1990s. Further, the share of wage earnings in GDP declined from 45 percent to 30 percent during the 1970s (to the benefit of the capital share), and it remained below 40 percent afterwards. It seems that an analysis based solely on data spanning the 1990s may miss important links between tariffs and wages. Here, we fill this gap by pursuing a historical investigation of these episodes of reforms.

Our findings confirm that trade reforms, ceteris paribus, have i) decreased wages and ii) increased wage inequality in Argentina. After controlling for individual worker characteristics, survey effects, industry effects, and time-varying skill premium effects (like labor regulation and unionization or school quality) we find a strong positive association

> 2The work by Attanasio, Goldberg, and Pavcnik (2004) and Golberg and Pavcnik (2005a) is similar to ours in that it exploits data from the eighties and the nineties. There is a major difference, though. Whereas their study involves one trade reform, we study two episodes of trade liberalization separated by a reversal to protection.

3
Lewis and Lockheed 4). The findings show a strong effect of GDP per capita, average years of schooling for women, and particularly ethnolinguistic fractionalization; the ethnolinguistic fractionalization and ethnolinguistic fractionalization squared combination is significant, a configuration that was consistently unimportant in the female primary completion rate regressions.<sup>7</sup> The model in column 2 suggests that the effect of ethnolinguistic fractionalization is exponential, since higher levels of heterogeneity show stronger positive effects on gender disparity in completion. Education expenditure is unrelated to the gender gap in primary school completion (columns 2-4). For the small data set, higher levels of female schooling and a socialist history are both associated with smaller gender gaps.

# **Table 6.  Determinants of gender disparity in primary school completion**

||Differen<br>|ce between male an<br>|d female primary<br>|completion rates<br>|
|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|
|GDP per capita (log)|–2.93***|–3.18***|||
||(2.88)|( 3.46)|||
|Ethnolinguistic fractionalization|10.61***|–18.32*|17.88***|16.43***|
||(3.01)|(1.77)|(4.42)|(3.52)|
|Ethnolinguistic fractionalization squared||31.69***<br>(2.72)|||
|Average years of schooling, female (age 25+)||||–2.93*** (4.49)|
|Education expenditure (percentage of GDP)||–0.32|–0.44|0.13|
|||(0.92)|(1.39)|(0.24)|
|Female labor force participation rate|||0.10<br>(1.41)||
|Socialist dummy||||11.59*** ( 4.86)|
|Road density (total network/land area)|||0<br>(0.45)|0.02*<br>(1.89)|
|Rural population (percentage of total population)|0.04<br>(1.15)||||
|Constant|20.77**|30.52***|–7.04**|2.75|
||(2.06)|( 3.60)|(2.44)|(0.77)|
|Number of observations|111|97|94|53|
|_R_<br>_2_|0.39|0.46|0.3|0.60|

* Significant at the 10 percent level.

** Significant at the 5 percent level.

*** Significant at the 1 percent level. _Note:_ Figures in brackets are _t-_ statistics.

**_Heterogeneity and learning._** Heterogeneity would also be expected to affect learning outcomes, through mechanisms operating between schools (to reduce school inputs) and within schools (to discriminate against “minority” children). The other variables explaining school completion would also be expected to influence learning. However, for estimating correlates of achievement at the country level, it is important to take into account differences across countries in the share of children in school. In countries in which not all children attend school, those who continue in

> 7 Unlike the high correlation between the female primary school completion rate and both GDP per capita and average years of schooling, the gender disparity in the primary school completion rate variable is not highly correlated with either of them.

22
working-age population with college by their corresponding employment rate, separately for men and women. Similarly, to obtain the share of unskilled workers, we multiply the share of working-age population with less than college in each country by their corresponding employment rate, separately for men and women. We then follow Pennings (2022) and assume that in most countries there are 100 women and 100 men to simply aggregate employment rates for men and women within each level of skill. In Qatar and United Arab Emirates, which have large populations of migrant workers, we adjust for the relative size of the male and female populations.

**Results with the current skill distribution.** We consider first an increase in employment rates to close gender gaps for _both_ skilled and unskilled women, using existing education rates. Recall that from Figure 7, most countries in MENA have larger gender employment gaps for unskilled women, and so closing gender employment gaps will lead to large increases employment among the unskilled, reducing average skill rates.

The left panel of Table 1 presents the impact of this change on the skill distribution _s_ (summarized by the share of skilled workers), and aggregate labor supply ( _L_ ), capital per worker ( _k_ ) and output per worker ( _y_ ). Consider the case of Egypt. Closing the gender gap in Egypt would decrease the share of skilled workers in 22% while increasing the number of workers in 56% (columns 1 and 2). If the stock of physical capital remains fixed, output per person would increase by 42% (column 7). If the stock of capital fully adjusts to the shock, the total effect on standards of living would be only 5pp higher (from 42 to 47%).

**Table 1:** Aggregate effects from closing gender employment gaps relative to the baseline (baseline scenario = 1).

|Country|_s_|_L_|_k_|_y_|_wU_|_wS_|Output<br>per<br>person.<br>Short<br>run.|Output<br>per<br>person.<br>Long run.|Output<br>per<br>person.<br>Change<br>in _L_<br>alone.|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**YEM**|0.63|1.84|0.890|0.890|0.915|1.308|1.593|1.638|1.84|
|**TUN**|0.83|1.49|0.958|0.958|0.954|1.120|1.378|1.428|1.49|
|**EGY**|0.78|1.56|0.942|0.942|0.938|1.168|1.416|1.470|1.56|
|**DZA**|0.83|1.62|0.958|0.958|0.954|1.120|1.484|1.552|1.62|
|**QAT (adj)**|1.06|1.09|1.013|1.013|1.015|0.965|1.093|1.104|1.09|
|**PSE**|0.83|1.69|0.963|0.963|0.948|1.127|1.536|1.627|1.69|
|**IRN**|0.81|1.63|0.961|0.961|0.939|1.148|1.482|1.567|1.63|
|**ARE (adj)**|1.06|1.16|1.005|1.005|1.022|0.958|1.137|1.166|1.16|

15
current policy debate focuses on calculating how much financial support is needed to achieve internationally-agreed development goals for reducing poverty and improving key social indicators, namely, the Millennium Development Goals (MDGs) adopted by the United Nations Assembly in 1999.

The results of this experiment are summarized in Table 5. They indicate that, through the channels highlighted in the previous experiments, a permanent increase of 5 percentage points of the aid-GDP ratio could lead to a reduction in the proportion of poor by about 20 percentage points between 2002 and 2015, if either the household survey linkage is used or the partial growth elasticity is -1.8. One of the main mechanisms, of course, is through an increase in public investment, which rises by about 4.9 percentage points (in terms of GDP) in the long run. The growth rates of real GDP per capita and disposable income follow an inverted U-shape pattern. Despite the sharp increase in aid, there is no evidence of a “Dutch disease” effect; in fact, the real exchange rate depreciates (because the increase in domestic supply puts downward pressure on domestic prices), leading to higher exports (which increase by about 4.4 percentage points of GDP in the long run). Higher income and tax revenue lead to a lower budget deficit (despite the increase in spending), and a large drop in the external debt-GDP and debt service-exports ratio in the long run. With a partial elasticity of -0.5, however, poverty drops by only 6 percentage points during the simulation period. If the actual elasticity is indeed close to -0.5 (and there are indications that this may well be the case), these results suggest that to reduce poverty by 20 percentage points between 2002 and 2015 the increase in aid should amount to almost 17 percentage points of GDP. This estimate is, of course, only a rough order of magnitude, given that it does not account for nonlinearities. But it does show how dramatically different the results can be, depending on the assumed elasticity between poverty and changes in consumption per capita.

In the above experiment, the increase in foreign aid was assumed to be allocated between food aid and nonfood aid according to initial shares. Table 6 shows the results of a permanent, 5 percentage point increase in total foreign assistance as a share of GDP that takes the form of an increase in nonfood aid only; 40
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7 regulatory policies aimed at strengthening market-based incentives in the domestic product and service markets, labor markets, trade, and capital and financial markets, among others. However, reforms may also involve actions to address market failures (such as the increased emphasis on effective financial sector regulation since the crisis) or other government policies that could affect productivity more directly.

The paper uses the IMF Monitoring of Fund Arrangements (MONA) database, which is publicly available and covers all aspects of the program conditionality. The MONA database provides a cumulative history of Fund-supported programs from Executive Board approval through its completion. The Monitoring of Fund Arrangements (MONA) database contains comparable information on the economic objectives and outcomes in Fund-supported arrangements. It tracks the performance of countries in terms of scheduled purchases and reviews, quantitative and structural conditionality, and macroeconomic indicators. Data are available for most arrangements since 2002 to the present and are collected at the time of arrangement approval and following each review. The data set covers 94 countries with an IMF arrangement program since 2002.

Most IMF financing features disbursements made in installments that are linked to the Board’s approval or review. Program reviews provide a framework to assess periodically whether the IMF-supported program is on track and whether modifications are necessary to achieve the program’s objectives. Reviews combine a backward-looking assessment (were the program conditions met per the agreed timetable?) with a forward-looking perspective (does the program need to be modified considering new developments?).

Program approval or reviews are based on various policy commitments agreed with the country authorities. Conditionalities could take different forms, including prior actions (PA), quantitative performance criteria (QPC), indicative targets (IT) or structural benchmarks (SB). Prior actions are measures that a country agrees to take before the IMF’s Executive Board approves financing or completes a review. They ensure that the program has the necessary foundation to succeed or is put back on track in the event of deviations from agreed policies. Examples include the elimination of price controls or formal approval of a budget consistent with the program’s fiscal framework. Quantitative performance criteria (QPCs) are specific