rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2: Sample characteristics at baseline**

|||**Means by trea**|**tment group**|**p-value for testing difference**||
|---|---|---|---|---|---|
||**Variable**|**PBF**|**Control**|**between PBF and control**<sup>**a**</sup>|**N**|
||Khatlon region|0.70|0.68|0.95|210|
||Any affiliated health houses|0.69|0.77|0.44|210|
||Number of health houses|1.60|2.49|0.08*|210|
||Catchment population|5030|5612|0.57|201|
|Rural|Number of physicians|1.56|1.34|0.72|210|
|Health|Number of midwives|1.03|1.10|0.72|210|
|Centers|Number of nurses|3.98|3.42|0.65|210|
||Laboratory|0.17|0.24|0.54|210|
||Improved source of water|0.72|0.73|0.89|210|
||Share of general equipment|0.74|0.83|0.24|210|
||Share of essential drugs|0.26|0.29|0.75|210|
||Male|0.39|0.38|0.89|1045|
||Age|42.63|42.34|0.83|1045|
||<br>Physician|0.23|0.22|0.90|1045|
||Midwife|0.11|0.11|0.89|1045|
|Health|Nurse|0.45|0.39|0.38|1045|
|Workers|Years of experience|18.63|18.07|0.68|1045|
||<br>Monthly income (in TJS)|634|670|0.24|1037|
||Days absent in past month|0.34|0.69|0.12|1045|
||Hours worked in past week|22.37|26.53|0.37|1045|
||Patients seen in past day|3.83|8.14|0.004***|1044|
||age|26.49|26.58|0.73|2829|
||<br>Lowest wealth quintile|0.19|0.15|0.56|2829|
||<br>Low wealth quintile|0.21|0.16|0.30|2829|
||<br>Middle wealth quintile|0.23|0.18|0.05*|2829|
||<br>High wealth quintile|0.19|0.22|0.45|2829|
|Recently|Highest wealth quintile|0.19|0.29|0.26|2829|
|pregnant|At least secondary education|0.60|0.60|0.99|2829|
|women|married|0.98|0.97|0.30|2829|
||Number of children|2.38|2.27|0.23|2733|
||Received any ANC|0.93|0.86|0.25|2829|
||<br>Timely ANC|0.74|0.68|0.30|2509|
||<br>At least 4 ANC visits|0.52|0.62|0.49|2826|
||Received PNC|0.57|0.67|0.14|2829|
||Male|0.47|0.45|0.05*|5038|
||Age|54.60|54.91|0.39|5038|
||Lowest wealth quintile|0.22|0.14|0.28|5038|
||<br>Low wealth quintile|0.22|0.17|0.28|5038|
|Adults|Middle wealth quintile|0.22|0.19|0.30|5038|
|aged 40|<br>High wealth quintile|0.19|0.23|0.42|5038|
|<br>and above|<br> <br>Highest wealth quintile|0.16|0.28|0.18|5038|
||<br>At least secondary education|0.65|0.62|0.71|5038|
||<br>Married|0.88|0.85|0.02**|5038|
||Blood pressure measured in|||||
||past year|0.50|0.45|0.50|5038|

Data from Baseline survey. a The tests are based on ordinary least square regressions with standard errors clustered at the district level. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 32
6 National frontiers could create several types of obstacle to financial intermediation, including controls on foreign exchange movements, risk of changes in foreign exchange rates, lack of knowledge about counterparties 8 associated with language differences and limited trading relations. Some of these problems are well dealt with by the CFA unions, but some are not and the net result is that no effective union-wide regional financial market exists.

Where the CFA unions do function reasonably well is in the area of exchange controls and foreign exchange risk, though even here there are problems. In particular it has not always been as easy in practice to transfer funds from 9 CFA countries to France in recent years as it has been in theory. Transfers of funds between the CFA countries are much freer especially since there are only two currencies involved: transfers between two countries of the UMOA involve no foreign exchange transaction; likewise between two BEAC zone countries; nevertheless, there is always the residual risk that a member country might, at a later date, impose restrictions on foreigners attempting to repatriate funds. The fixed exchange rate reduces but does not eliminate the risk of foreign exchange losses: balance of payments difficulties have, 8 Note, however that the reliability of official statistics which indicate a rather low level of intra-regional trade (Table 5) have been questioned by Yeats (1989b). In line with some studies of colonial relationships (cf. Austen (1987)), the substantial trade with France has Drompted some to suggest that the existence of the zone provides extensive benefits to France. A recent study on these lines is Yeats (1989a).

9 There are some limitations which mean that capital is not fully free to flow to France.

First, transfers are increasingly being delayed or even (in some countries) refused except where the bank can provide the funds abroad itself. This is probably the most important limitation.

Second, banks are not permitted to hold more than working balances outside their own country. In some cases this has been stretched to imply that banks must always use to the full all foreign lines of credit avail able to them.

Third, insurance companies are only allowed a very restricted range of investment possibilities outside their own country.

Fourth, a transfer fee of 0.5% has been payable to the BCEAO on all transfers (for current or capital gayments) made to France. This hardly discourages flows of a long-term craracter, and is said to be bypassed by many banks wherever possible.

Fifth, export receipts must be repatriated to the exporters' country. (This serves to block one possible way of avoiding the transfer fee).

Sixth, there have been requirements to declare export of CFA currency notes; these however are largely ineffective as the currency notes have been freely acceptable in Europe.
23 20. Barberis NC. Thirty Years of Prospect Theory in Economics: A Review and Assessment. J Econ Perspect. 2013 Feb;27(1):173–96.

21. Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 1979;47(2):263–91.

22. StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC; 2017.

23. Lee, D. S. 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Review of Economic Studies 76: 1071-1102.

24. Barnabas RV, van Heerden A, McConnell M, Szpiro AA, Krows ML, Schaafsma TT, et al. Lottery incentives have short‐term impact on ART initiation among men: results from a randomized pilot study. J Int AIDS Soc. 2020 Jun 26;23(Suppl 2):e25519.

25. Chamie G, Schaffer EM, Ndyabakira A, Emperador DM, Kwarisiima D, Camlin CS, et al. Comparative effectiveness of novel nonmonetary incentives to promote HIV testing. AIDS Lond Engl. 2018 Jul 17;32(11):1443–51.

26. Balampama M., de Walque, D., Dow W.H. and Hemono. R. Influence of COVID-19 on Female Sex Workers in Dar es Salaam, Tanzania: A Mixed-Methods Analysis. 2023. Unpublished manuscript.
**<u>Table A.29. The district fixed-effect RDD-DD regressions of households’ income using a poverty rate bandwidth of 10%</u>**

|||||Dependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|-0.0833|-0.3695|1.0748***|-0.4384*|1.0473|0.8605**|1.4858**|
||(0.1215)|(0.7388)|(0.3877)|(0.2360)|(0.9295)|(0.3744)|(0.6418)|
|(Poverty rate – 50) *Post-program|0.0056|-0.0741*|-0.0660*|0.0104|-0.0305|-0.0571*|-0.0159|
|period|(0.0087)|(0.0442)|(0.0383)|(0.0215)|(0.0553)|(0.0318)|(0.0408)|
|Program * (Poverty rate – 50) *Post-|0.0001|0.2270*|-0.0024|0.0005|-0.2262|0.0734|-0.0446|
|program period|(0.0190)|(0.1198)|(0.0901)|(0.0374)|(0.1743)|(0.0781)|(0.1175)|
|Age of household heads|0.0303***|0.0409**|0.0501***|0.1466***|-0.0465***|-0.0853***|-0.0584***|
||(0.0022)|(0.0160)|(0.0091)|(0.0151)|(0.0112)|(0.0118)|(0.0117)|
|Age squared of household heads|-0.0003***|-0.0007***|-0.0006***|-0.0014***|0.0007***|0.0012***|0.0008***|
||(0.0000)|(0.0002)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0119|-0.0816|0.1754**|0.7573***|-0.4447***|-0.0625|-0.4033***|
|female=0)|(0.0205)|(0.1189)|(0.0713)|(0.0857)|(0.0577)|(0.0657)|(0.0771)|
|Ethnic minorities (yes=1, Kinh=0)|-0.6126***|-0.1217|-1.7998***|0.7210***|-0.5323***|0.6820***|-0.7981***|
||(0.0535)|(0.1973)|(0.1868)|(0.1232)|(0.1620)|(0.1358)|(0.1115)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.5328***|3.2504***|1.7557***|3.4376***|4.9524***|1.7241***|3.1431***|
||(0.0800)|(0.3780)|(0.2682)|(0.3926)|(0.3479)|(0.3670)|(0.2875)|
|Observations|28,796|28,796|28,796|28,796|28,796|28,796|28,796|
|R-squared|0.338|0.152|0.095|0.153|0.204|0.142|0.092|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

79
The focus is on medium-term economic growth (5-year periods) thereby mitigating potential biases due to adaptation. The effects of the different natural disasters (i.e. droughts, floods, earthquakes and storms) are examined separately by economic sector (agriculture, industry, and services), each time controlling for a series of well known growth determinants. This way the paper broadens the scope of the existing literature, which has so far largely concentrated on aggregate measures of disasters and/or economic activity. This disaggregated approach also yields preliminary insights in the distributive effects of natural disasters. Through the use of the dynamic panel GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) great care is taken in addressing endogeneity issues related to the potential correlation between explanatory variables and unobserved country-specific factors.

To maintain consistency with other studies, the data on natural disasters are obtained from the Emergency Disasters Database (EM-DAT) database of the Centre for Research on the Epidemiology of Disasters (CRED). The share of the population affected by a specific type of disaster over a given period of time is taken as measure of natural disaster. This way, both the _frequency_ and _intensity_ of the disaster are reflected. The sample spans 94 developing and developed countries over the period 1961-2005.

The empirical results are consistent with the implications from the traditional Solow-Swan model. Three major conclusions emerge. First, different disasters affect growth in different economic sectors differently and the insights obtained with overaggregation are misleading. Second, while moderate disasters can have a positive growth effect on certain sectors, severe disasters don’t. Third, growth in developing countries is more sensitive to natural disasters—more sectors are affected, the magnitudes are nontrivial, and the poor are likely to be more affected by disasters (both positively and negatively).

To motivate the disaggregated approach and facilitate the interpretation of the empirical results Section 2 proceeds by reviewing the Solow-Swan growth model. Section 3 discusses the estimation methodology and section 4 elaborates on the growth determinants and the natural disaster data used. The empirical findings are discussed in section 5. Section 6 concludes.

4
# **I – Introduction**

According to the most recent World Bank Enterprise Survey Data, firms in developing countries report that access to finance is the biggest obstacle for the growth of their operations. Across all regions, 17 percent of firms report that access to finance is the biggest obstacle. In some regions, access to finance is an even larger obstacle. For example, in Sub-Saharan Africa close to a quarter of the firms report access to finance to be the top obstacle.

An important impediment to firm financing is asymmetric information: a firm seeking to borrow from a lender in the credit market has better information about its financial state and its ability and willingness to repay the loan than the lender. Asymmetric information can lead borrowers less seriously intent on repaying loans to be more willing to seek out loans (adverse selection) and borrowers to use loaned funds in ways that are inconsistent with the interest of the lender (moral hazard). Seminal work by Stiglitz and Weiss (1981) shows that under asymmetric information the equilibrium interest rate is such that demand for credit exceeds supply - even borrowers willing to pay the market equilibrium interest rate are not able to get a loan (credit rationing).

Credit information sharing schemes are mechanisms that can help lenders and borrowers overcome asymmetric information problems because the schemes allow lenders to share with other lenders information about their clients, either through a privately held credit bureau (CB) or publicly regulated credit registry (CR). Such credit information schemes disseminate knowledge of payment history, total debt exposure, and overall credit worthiness, thus bridging the information divide between lenders and borrowers.

The theoretical literature on credit information argues that credit information sharing schemes can increase firm financing and lower financial constraints. In particular, theoretical 2
||||Russ|ia 2012 (rec|all)|||
|---|---|---|---|---|---|---|---|
||Mean<br>0.415|SD<br>0.186|p(10)<br>0.152|p(25)<br>0.295|p(50)<br>0.432|p(75)<br>0.545|p(90)<br>0.649|
|Data driven performance monitoring|0.299|0.218|0|0.133|0.25|0.467|0.6|
|Incentives and targets|0.483|0.219|0.167<br>|0.35<br>Russia 2017|0.5|0.639|0.75|
||Mean|SD|p(i0)|p(25)|p(50)|p(75)|p(90)|
|Management score|0.432|0.192|0.152|0.306|0.45|0.567|0.675|
|Data driven performance monitoring|0.323|0.227|0|0.133|0.3|0.5|0.633|
|Incentives and targets|0.496|0.221|0.167|0.361|0.523|0.663|0.762|
||||Croatia|(Grover et al.|, 2019)|||
||Mean|SD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Management score|0.542|0.15|0.333|0.431|0.558|0.657|0.724|
|Data driven performance monitoring|0.501|0.163|0.300|0.367|0.483|0.617|0.75|
|Incentives and targets|0.562|0.191|0.298<br>Mexico (|0.44<br>Bloom et al.,|0.595<br> 2019b)|0.698|0.788|
||Mean|SD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Management score|0.42|0.201|0.125|0.275|0.44|0.576|0.673|
|Data driven performance monitoring|0.465|0.232|0.133|0.278|0.5|0.639|0.75|
|Incentives and targets|0.386|0.235|0|0.2|0.398|0.568|0.7|
||||Pakistan|(Lemos et al|., 2016)|||
||Mean|SD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Management score|0.443|0.174|0.206|0.311|0.453|0.571|0.666|
|Data driven performance monitoring|0.524|0.207|0.25|0.4|0.533|0.667|0.767|
|Incentives and targets|0.419|0.227|0.114<br>United Stat|0.227<br>es (Bloom e|0.432<br>t al., 2013)|0.603|0.712|
||Mean|SD|p(10)|p(25)|p(50)|p(75)|p(90)|
|Management score|0.615|0.172|0.379|0.521|0.648|0.742|0.806|
|Data driven performance monitoring|0.643|0.199|0.365|0.521|0.677|0.792|0.865|
|Incentivesandtargets|0.583|0.215|0.3|0.474|0.623|0.739|0.819|
# Contents

|Analysis<br>of Rural Credit Allocation"s........................|......e<br>2|
|---|---|
|Cooperatives<br>and Credit**Group.........4...................**|**........**<br>4|
|The Institutional<br>Design of Credit Groups.....................|.....<br>5|
|Dynamic Considerations ee....e...............................|16|
|Empirical<br>Ev<br>itence<br>**.........................................**|**20**|
|Conclusions.............................................|...........<br>22|
|lotes<br>**............**<br>***.. ......*.........**<br>**** go........**<br>***0*~**********.**|24|
|...................<br>**....**<br>**.......................................**|**....**<br>26|

Avishay Braverman is Division Chief of the Agricultural Policies Division of the Agriculture and Rural Development Department of the World Bank, Washington, DC. J. Luis Guasch is Associate Professor of Economics at the University of California, San Diego.
# Figure 8. Fiscal outcomes in EMDEs around oil price plunges

## A. Fiscal sustainability gap

<!-- Start of picture text --> Percent of GDP<br>Past oil price plunges Interquartile range<br>9<br>6<br>3<br>0<br>-3<br>-6<br>t-3 t-2 t-1 t t+1 t+2 t+3<br>Year<br><!-- End of picture text -->

## C. Overall fiscal balance

## B. Government debt

<!-- Start of picture text --> Percent of GDP<br>Past oil price plunges Interquartile range<br>80<br>60<br>40<br>20<br>0<br>t-3 t-2 t-1 t t+1 t+2 t+3<br>Year<br><!-- End of picture text -->

## D. Credit to the private sector

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>Past oil price plunges Interquartile range Past oil price plunges Interquartile range<br>8 50<br>40<br>4<br>30<br>0<br>20<br>-4<br>10<br>-8 0<br>t-3 t-2 t-1 t t+1 t+2 t+3 t-3 t-2 t-1 t t+1 t+2 t+3<br>Year Year<br><!-- End of picture text --> Source: International Monetary Fund, World Bank (2017a).

Note: Year t refers to the year of oil price plunges. Past oil price plunges include collapses in global oil prices in 1991, 1998, 2001, and 2008 (World Bank 2015). Simple averages of 36 EMDE oil exporters in all episodes. C. Samples are restricted to episodes where data on sustainability gaps are available.

28
occurred, as our methodology does (and as the CMMHH permits). Finally, the high-frequency phone surveys do not contain the needed level of detail on work arrangements —employer types and contractual arrangement— to distinguish pre-COVID PFPS workers in the core sectors from other private sector employees. Notwithstanding these limitations, we attempt to learn what we can by comparing outcomes for private sector wage workers to our projections.

# 5.3 Aggregate Projection Results

# **5.3.1 Jordan**

As shown in Table 5-1, using our methodology, for wave 1 (summer of 2020) we project the aggregate net rate of PFPS job losses in the core WBES sectors to have been 15.8 percent relative to the December 2019 baseline, which includes

|_Table 5-1: Jordan: Job Loss Projections_<br>_Levels, CFUWBES Waves 1 and 2 and_|_PFPS Jobs as_<br>_CMMHH, 2021_<br>As of Wave 1|_a Percent of P_<br> <br>Cumulative<br>Wave 2|_re-COVID_<br>CMMHH 2021|
|---|---|---|---|
|Modeled Job Losses: Percent of PFPS<br>jobs lost (relative to Dec 2019) as<br>projected|15.8|25.2|N/A|
|_Of which_: Due to Permanent closures|7.1|9.4|N/A|
|Survey-measured job losses: Percent of<br>PFPS Jobs Lost(relative to Dec 2019)|16.8|25.5|23.3|
|Source: Author estimates.||||

losses due to permanent firm closure of approximately 7.1 percent.

_Table 5-2 Jordan: Selected Potential Jobs at Risk Influence Variables, Means from CFUWBES_

|Variable|Wave 1|Wave 2|
|---|---|---|
|Temporarily closed due to COVID (weeks of closure )*|8.74|1.83|
|Percentage of firm workers currently working remotely<br>(percentage)|8.71|2.82|
|Sales change (percent change year-on-year, relative to the<br>same month in 2019)|-51.32|-49.71|
|Change in export share of sales (percentage difference from<br>2019 ES)|3.69|-2.62|
|Supply of goods and materials has decreased (1=yes,<br>compared to the same month in 2019)|0.81|0.85|
|Started/increased online or delivery business activities<br>(1=yes)*|0.57|0.13|
|Faced financial issues (1=yes)*|0.94|0.85|
|Received other policy support (1=yes)*|0.11|0.05|
|Received wage subsidies (1=yes)*|0.22|0.02|
|Infection risk (score) (0-300)|160.98|160.98|

Source: CFUWBES. Notes: The baseline of the variables is 2019 unless otherwise noted: * Indicates that the baseline of the variable in wave 1 is “since COVID outbreak” and its baseline in wave 2 is “since wave 1”. Unless otherwise indicated, the statistics do not include data from permanently closed firms or firms that did not answer the corresponding survey question. Weights from each round are applied.

27
and livestock) were obtained from previous studies and were incorporated into the analysis.<sup>2</sup> ' The TFP analysis of each farm was repeated using these social opportunity costs rather than the actual private costs. The results of these social efficiency estimates are summarized in table 8.

**Table** 8: Social relative total factor productivity **for different farm size categories***

|Region|Year|Average<br>Social/Private|Social To|tal Factor Produ|ctivity**|
|---|---|---|---|---|---|
|||TFP ratio|Small|Medium|Large|
|Eastern Free State|1988/89|0.86|138|133|100|
|Ruens|1987/88|0.91|135|108|100|
|Western Transvaal|1981/82|0.78|118|85|100|
|Eastern Free State|1979/80|0.75|121|97|100|

- Note: * Three farn size categories were defined for each data set: small represents the smallest third of the farms; medium represent the middle third of the farns; and laree represents the largest third of the farms.

- ** TFP index and labor capital ratio of large farms are the norms (100) against which the other size categories were compared.

The results from the social TFP analysis should be interpreted with care. Farmers react to the incentive structure facing them, and if capital are relatively cheaper, they should use more of it, and vice versa. For this reason the social TFP calculations are more indicative of the distortions than the actual social costs or efficiency losses. Strictly, changing the values from private to social prices does nothing to the physical input and output ratio, and TFP stays essentially the same, although the weighting of the inputs and outputs change. However, the point here is to determine to what an extent farm size influences the farmer's ability to capture benefits and use the structure of incentives. The results obtained from the social TFP analyses, which are summarized in Table 8, indicate that:

- Average social TFP is lower than average private TFP in all the regions. The difference is much more accentuated at the beginning of the 1 980s than later in the decade when some of the privileges were already removed. The reason for this is that because all farmers face the same prices, the value of outputs and inputs can be treated as quality adjusted quantities. While these differences are meaningless in terms of efficiency, they indicate to what an extent policies have been distorted.

- Larger farms are less efficient relative to smaller farms when social opportunity costs are used to determine the value of output instead of actual market prices. The reason for this stems mainly from the differences in the relative importance of labor and capital in the input rmix of large and small farms (see table 7). The value of output of small and large farms are generally affected in a similar

- 21 See, for example, Helm and Van Zyl (1994), Van Heerden and Van Zyl (1992) and Meyer and Van Zyl (1993).

30
# **FOOD SECURITY AND HEALTH SECURITY: EXPLAINING** THE **LEVELS** OF **NUTRITION IN PAKISTAN**

# **INTFRODUCTION**

Many of the most influential studies of malnutrition and public policy have focused on energy availability and consumption.' That is, there has been a tendency to equate hunger with malnutritior.. A number of recent studies by economists, however, have explored the relationship of factors other than food on the production of nutrition.2 In this they are joining the ranks of clinical nutritionists who have recognized for some time that nutrition is determined by infection as well as by levels of food consumption (Scrimzhaw, Taylor, and Gordon 1968; Mason et al. 1984).

The focus of this approach, then, is not on a single input - food security - but on the level of a number of inputs and the efficiency in which they are combined. Moreover, given that the manner by which households reduce infections or acquire health care often differs appreciably from the manner by which food is obtained, by viewing nutrition as an output in a production process, policy analysis can consider a broader set of interventions that may influence nutritional outcomes. Given that some inputs are public goods, the question can

**1** For example, Reutlinger, Shlomo, and Marcelo Selowsky, Malnutrition and Poverty, Baltimore, MD, U.S.A.: Johns Hopkins University Press, 1976.

**2** See, for example, the review by Behrman (1991) and Schiff and Valdes (1990).