rafmacalaba/datause-encoder-data huggingface dataset:
-29-

# In-Service Training and Suyervised Visits

2.52 The VEWs in the North West Province follow the standard T & V twoweek work plan. Of the 12 working days in a fortnight, the VEW spends 8 days making visits to groups, 1 or 2 days in training sessions at the TDC, and 1 day writing reports or doing other office work. That leaves one or two days for making up missed group visits due to public holidays or illness (MIDENO, nd1). 2.53 The fortnightly in-service training takes place at the TDC. The training sessions are led by staff of the TDC or the provincial subject matter specialists, The aim of these sessions, as in other T & V programs, ls to fully brief the VEWs on the agricultural tasks their farmers otigh t to be doing for the corning two-week period. Classroom sessions are reinforced with demonstrations on the TD; farm. The V'EUs are then expected to pass otl this message to each of their groups over the following fortnight.

2.54 In a review of 'JE perfortance (Table 9), there ware no significant differences between male and female VEW for any of the characteristics assessed by the supervisors.

**Table 9:** Ratings of VIWs **by** Gender*

|-----------------------------------------------------------------<br>**Dlmension**|-<br>**Provi**<br>**MalH**|**nCe**<br>Female|**Bu**<br>**Male**<br>|**t**<br>**Female**|Don&a-<br>**Male**|__---------_<br>Mantung<br>**Female**|_-----------------------<br>_Menw_<br>**male**<br>|--------------<br>_hum_<br>**Female**|**Mes**<br>**Male**|**am**<br>**Female**|**Mo**<br>**Male**<br>**F**|**"o**<br>**emale**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Duty conseLousness|**2.0**|**1.9**|**2.1**|**1.1**|**1.9**|**1.8**|**2.0**|**2.3**|**2.0**|**2.1**|**1.9**|**2.1**|
|**Time**consciousness|1. 9|1.7|2.0|1.3|1.9|1.8|10|<br>1.7|1.8|1.7|1.9|2.0|
|Technieal<br>**knowledge**|**2.0**|1.8|2.1|1.6|1.9|1.7|2.1|2.3|2.1|1.7|1.8|1.7|
|Rnlationship<br>wLth<br>**farmers**|1.9|**2.1**|**2.2**|1.9|1.9|**2.1**|**2.1**|**2.0**|1.8|**2.3**|1.7|1.8|
|Creativity|1.7|1.6|**1.8**|1.4|1.7|1.5|1.3|14|**1.9**|1.9|1.I|1.5|
|**AVERAGZ**|**1.9**|**1.8**|**2.0**|**1.5**|**_1.9_**|**1.0**|**1.9**|**1.9**|**1.9**|**1.9**|**1.8**|**1.8**|
|Numberinsample|223|55|54|7|40|9|35|6|65|23|29|10|

mius: **PEN Division,** MIDENO o **Ratings are based** on **a** four-point **scale** with 0 being **'bad' and** 3 being 'very good'.

# Innovations in Extension Strategy

2.55 The T & V model is much less explicit on the strategy of extension than it is on the organization and structure of an extension servic._. It emphasizes that extension should rely on contact farmers rather than groups serving as models to neighboring farmers (Benor and Harrison, 1977: 13-14). Moreover, the T & V model is silent on issues of gender.
- 20 -

although the exogeneity hypothesis of _D_<sup>_TE_</sup> cannot be rejected formally by the Hausman test (the test statistics is negative). The impact of adopted technical evaluation systems is also far from significant; the coefficient is positive (0.162).

|Table 6. Two-Stage Probit Le|ast Squares M|odel for En|try and Tech|nical Evaluation|
|---|---|---|---|---|
|Estimation method|Two-stage pr<br>|obitleast squa<br>|res<br>||
|Dependent variable|1st stage<br>ln_n_|1st stage<br>_D _<sup>_TE_</sup>|2nd stage<br>ln_n_|2nd stage<br>_D _<sup>_TE_</sup>|
|ln_n_||||2.589 <sup>***</sup><br>(0.737)|
|_D _<sup>_TE_</sup>|||0.162||
||||(0.874)||
|_D _<sup>_DAM_</sup>|–0.170|–0.666|–0.062|–0.039|
||(0.153)|(0.727)|(0.637)|(0.749)|
|_D _<sup>_CIVIL_</sup>|0.451 <sup>***</sup>|0.984 <sup>***</sup>|0.291|0.024 <sup>***</sup>|
||(0.109)|(0.384)|(0.894)|(0.490)|
|ln_GCAP_|–0.010 <sup>*</sup>|0.038 <sup>*</sup>|–0.016|0.085 <sup>***</sup>|
||(0.005)|(0.021)|(0.035)|(0.020)|
|ln_LINE_|0.023 <sup>***</sup>|–0.012|0.025 <sup>**</sup>|–0.066 <sup>***</sup>|
||(0.005)|(0.019)|(0.012)|(0.023)|
|ln_SUBS_|0.005|0.044 <sup>**</sup>|–0.002|0.026|
||(0.005)|(0.020)|(0.041)|(0.022)|
|ln_COST_|0.110 <sup>***</sup>|–0.205|0.144|–0.613 <sup>***</sup>|
||(0.039)|(0.155)|(0.193)|(0.154)|
|ln_WAID_|–1.856 <sup>***</sup>|1.637|–2.122||
||(0.579)|(2.427)|(1.475)||
|ln_CAID_|–0.009 <sup>***</sup>|–0.054||0.187|
||(0.047)|(0.158)||(0.150)|
|Constant|17.761 <sup>***</sup>|–13.235|19.910 <sup>*</sup>|–2.189 <sup>*</sup>|
||(5.124)|(21.465)|(12.225)|(1.277)|
|Obs.|131|131||131|
|_R_-squared|0.462||||
|_F_-statistics|10.30||||
|LR chi2||64.17||49.83|
|Recipient country dummy|China|China|China|No|
|Bidder nationality dummy|No|No|No|No|
|Donordummy|Yes|No|Yes|No|

Note that the standard errors are shown in parentheses. *, **, and *** indicate the 10%, 5% and 1% significance levels, respectively.

# **_Entry and bids_**

The instrumental variable model where the equilibrium bid function is estimated with the number of bidders _n_ instrumented shows more results on the bidder entry preferences. The first stage regression on ln _n_ indicates that fewer bidders would participate in the auctions for dam construction, and civil works and more valuable contracts could attract more participants (Table 7). Large-scale power transmission and distribution projects look more attractive for potential contractors, but there is no significant evidence that larger generation and substation works could have more bidders. This is consistent with the above two-stage
_8_ _WATER WHEN IT COUNTS_ To set up the measurement system, we fully mapped all plots within the three study schemes. During this exercise, we identified all plots that draw water from sprinklers, canals, or hydrants connected to the scheme; the source of water for these plots; and information on the household of the person in charge of cultivating them. During the initial contact with the water users, we identified 153 plots belonging to 120 households across the three schemes.

# _Irrigation water availability_

We employ a low-cost, community-centered approach to monitor water supply at the plot level. As part of the initial scheme listing, all water diversion points and their water catchment areas were mapped, and water measurement points were identified and calibrated. This calibration allowed us to convert simple measurements, such as water depth in a canal (measured with a ruler) or number of sprinklers on a plot, into volume of water diverted to the plots from that point. Each scheme was assigned a dedicated data collector who took the established measurements at each point three times a day.<sup>1</sup> These measures were then converted to flows through canals, sprinklers, and hydrants. From these, we estimated the volume of water delivered to each plot in a week. We obtain our measure of water availability by dividing that volume by the area of the plot. Water availability is therefore reported as a a depth measured in mm/week.<sup>2</sup><sup>_,_3</sup>

# _Agriculture surveys_

We additionally conduct household surveys every four months to capture detailed

> 1These data collectors were farmers from the schemes who received training and a nominal payment to collect measurements.

> 2This depth is interpretable as the maximum depth of water that could cover a plot if the full amount of water delivered in that week is distributed equally across the area of a cultivated plot.

> 3A detailed description of the monitoring methods and imputation protocols for translating measurements into estimates of volumetric flows is included in appendix A.A2. A full description of measurement methods being developed and tested around the world by technical partners contributing to the study is available at: http://www.imomohub.org/.
<!-- Start of picture text --> Indonesia<br>100<br>80<br>60<br>40<br>20<br>0<br>1981 1986 1991 1996 2001 2006<br>(Gs Fuel exports(% of merchandise exports) [-*] Food exports (% ofmerchandiseexports)<br>Gs Manufactures exports (% of merchandise exports)<br>Scale: Units<br><!-- End of picture text -->
foreign ownership may come with foreign values and culture that tend to be more favorable for women’s employment. Women may be particularly sensitive to the law and order situation and the prevalence of crime as criminals tend to target women more than men (Glaeser and Sacerdote 1999; Islam 2013).

Based on the discussion in the previous paragraph, we control for the following variables: a dummy variable equal to 1 if the firm has one or more female owners and 0 otherwise; proportion of firms’ ownership that is with foreign individuals or companies ( _Foreign Ownership_ ); a dummy variable equal to 1 if the firm experienced losses due to crime, theft and disorder and 0 otherwise ( _Crime Losses_ ); and a measure of the quality of courts based on a question in the ES on how severe is the (lack of proper) functioning of courts as an obstacle for firms’ current operations (on 0-4 scale). Like for exports, we use cell averages at the country-ES round-industry level of the responses to the question on courts’ functioning ( _Courts Obstacle_ ).

The remaining firm-level controls are intended to capture recent investment by the firm in physical capital and various aspects of the business environment as experienced by the firm. The assumption here is that recent investment and business environment are likely to impact firm efficiency and therefore its ability to compete in international markets. If the share of female workers also happens to vary systematically with the quality of the business environment, our main results for the relationship between exports and female workers could suffer from omitted variable bias problem. For recent investment, we use as a control a dummy variable equal to 1 if the firm purchased fixed assets during the last year and 0 otherwise. For the business environment we control for hours of power outages experienced by the firm in a typical month in the last year ( _Power Outages_ ); a dummy variable equal to 1 if the firm was inspected by tax officials during the last year and 0 otherwise ( _Firm Inspected_ ); and severity level (on 0-4 scale) of the following 15
- Glitz, Albrecht (2012). “The Labor Market Impact of Immigration: A Quasi-Experiment Exploiting Immigrant Location Rules in Germany,” _Journal of Labor Economics_ , Vol. 30 (1), pp. 175-213.

- Hunt, J. (1992). “The impact of the 1962 repatriates from Algeria on the French labor market," _Industrial and Labor Relations Review_ , pp. 556-572.

- Kondylis, Florence (2010). “Conflict displacement and labor market outcomes in post-war Bosnia and Herzegovina,” _Journal of Development Economics_ , 93, 2, 235-248.

- Kreibaum, M (2014). “Their Suffering, Our Burden? How Congolese Refugees Affect the Ugandan Population ,” HiCN Working Paper 181.

- Mabiso A., J.-F. Maystadt, K. Hirvonen, J. Vandercasteelen (2014). “Refugees and Food Security in Host Communities: A Review of Impacts and Policy Options to Enhance Resilience,” International Food Policy Research Institute (IFPRI).

- Maystadt, Jean-François and Gilles Duranton (2014). The Development Push of Refugees: Evidence from Tanzania. LUMS Economics Working Paper Series 2014/019.

- Maystadt, Jean-François and Philip Verwimp (2014). “Winners and Losers Among a Refugee-Hosting Population.” Economic _Development and Cultural Change_ , 62(4): 769-80

- Ӧzden, Caglar, and Mathis Wagner (2015). “Immigrants versus Natives: Displacement and Job Creation.” World Bank Working Paper Series.

- Mattoo, A., I.C. Neagu and Ç. Özden (2008). “Brain Waste? Educated immigrants in the US labor market,” _Journal of Development Economics_ , 87 (2), 255-269.

- Peri, G. (2012) “The Effect of Immigration on Productivity: Evidence from U.S. States,” _Review of Economics and Statistics_ , vol. 94(1), pages 348-358, 07.

- Peri, G. and C. Sparber (2009). “Task Specialization, Immigration, and Wages,” _AEJ:_ Applied Economics, Vol. 1, No. 3, pp. 135-169.

- Peri, Giovanni and Vasil Yasenov (2015). “The Labor Market Effects of a Refugee Wave: Applying the Synthetic Control Method to the Mariel Boatlift,” NBER Working Paper. No. 21801.

- Ruiz, I. and C. Vargas-Silva (2013). “The Economics of Forced Migration,” _Journal of Development Studies_ , 49 (6): 772–84.

- Ruiz, I. and C. Vargas-Silva (2015). “The Labor Market Impacts of Forced Migration,” _AER: Papers & Proceedings_ , 105(5): 581–586.

- Tumen, Semih (2015). “The use of natural experiments in migration research,” IZA World of Labor 2015: 191.

- UNHCR (United Nations High Commissioner for Refugees) (2014). “UNHCR Global Trends 2014: World At War.”

World Bank (2015). _Good Jobs in Turkey_ . World Bank Publication, Washington DC.

31
compliance runs though bureaucrats expected disutility from being publicly exposed. An alternative understanding is that the Sala IV’s efforts generated an honest reevaluation within agencies about their own processes of compliance, a reevaluation entirely disconnected from the potential (external) concerns of a disapproving public. Adjudicating among these two alternatives will likely require data on bureaucratic responses to the press conference, data that might reveal the extent to which agents engaged the public with their efforts to rectify their problems. It is also unlikely that distinct public entities responded in the same ways. If the public regarding account is correct, we should be most likely to see public responses from responsible authorities that are most directly accountable to voters, e.g. ministries.

A number of open questions remain. For one, we might ask why the court would issue unclear orders when the clarity of those orders seems to be powerfully related to noncompliance. We might wish to know if police patrol monitoring itself has an impact on compliance. Originally, the Sala IV had only monitored cases in which responsible authorities know they were being monitored. Recently, the CJC completed an effort to go back and monitor the outcome of a sample of cases about which it had not previously informed the responsible authorities. The results of this study will help quantify the effect of direct monitoring.

We might also ask what impact the system has had on the Sala IV itself. What has it learned? Have these lessons influenced their jurisprudence? Have they affected their political relations with other actors in the Costa Rican state? And surely it is worth asking about the ways in which direct monitoring has influenced litigants. In what ways, if any, did the initial findings mobilize civil society? Has there been an impact on the mix of cases coming to the Sala IV?

30
10

# 3. CONTCON OF THE TS REFORMS '

# A. ltial Macroeconomic _CoSdiou_

As table 3 shows, trade reforms were implemented during a period of widespread economic crises for most of the countries, which were suffering under large debt overhangs, declining output, balance-of- payments problems, falling foreign exchange reserves, and high inflation. Of course, the intensity of each of these problems varied across these countries. For example, in the year immediately before the start of the reforms, inflation rates in Bolivia were in excess of 1,000 percent, while they were about 60 percent in Ecuador. In the five years preceding the reforms, real output in TABLE 3: **THE EXTENT** OF ECONOMIC **CRISIS** AT **THE** START OF **THE** TRADE REFORMS

||BoP<br>Problem**(1)**|**Declining Foei**<br>**Exch. Resrves**|**HIgh**<br>**flatdon M)**|**Economi**<br>**Decline**(3)|Debt<br>Problem (4)|
|---|---|---|---|---|---|
|**Argentina**|||**a**||*|
|Bolivia|a||a|||
|Brazil|||||a|
|uile|~~~~*|||a||
|Colombia|a|a||a|a|
|Costa Rica|a|a||e|a|
|Ecuador|a|a|a|a|a|
|Guatemal|a|||a|**a**|
|Honduras|a|a|||**a**|
|Jamaica|*|a||a|a|
|Mexico|a||a|a||
|Paraguay||a|||a|
|Peru|a|a|a|a||
|Trinidad & Tobago|*|a||a|a|
|Urguay|a||e|a|a|
|Venezuela|a|*|||**a**|
|No. of Countries|16|10|7|12|16|

1/ increasing or consistent lrge curent account and/or balnce of paymens deficits V inflation in the year preceding reforms in exceu of 50 percent Bolivia had tbe highest rates, in excess of 1,000 perent, with Pern following with 667 percent, Brazil with 145 percent, A ninm with 131 percent nd Ecuador, Mexico _and_ UNguay between 50 and 80 percent.

3/ negative or decreasing real GDP growth rtes.

4/ debt problem aid to exist if a country had falen back on debt _payments_ and/or had to reschedule or refinance debt.

_Source:_ World Bank (various repoalu)
- 1) Marriage is restricted to endogamous groups, i.e.: people are only permitted to marry within a well-defined set of families who make up their sub-caste.

- 2) They are patrilocal - brides leave their parents' home to live with their husbands. Marriages are arranged for both grooms and brides by their parents, and the preferences of the parents usually drive all the choices and arrangements.

- 3) Marriage is considered final and, while there are cases of separation, divorce is not an option.

- 4) The burden of celebrating a marriage almost always falls on the parents of the bride.

There is a considerable amount of variation between sub-castes in their marriage and kinship patterns (Karve, 1965). While most prohibit marriage outside the sub-caste, the size of the group can vary a great deal. Some communities in Southern India prefer marriage within a circle of close relatives, usually between a man and the daughter of his elder sister, or between the children of brothers. Such consanguineous marriages are getting increasingly rare because suitable grooms are difficult to find. Another characteristic of Indian kinship systems is the variation in their prohibition against marrying partners who belong to the same village. In the language of the kinship studies, such communities who marry outside their own village are called "village exogamous." In Karnataka State, where these data are from, the northern districts show a tendency towards village exogamy, while in the southern districts the majority do not prefer it and may even have prohibitions against it. Such preferences and prohibitions are prescribed by the customs of the community and are exogenous to the choices of households who may suffer social sanctions by violating them.

Getting one's daughter married, within these constraints, is considered an Indian parent's primary duty; to have an older unmarried daughter is a tremendous misfortune with large social and economic costs. The costs of getting a daughter married, however, have been steadily rising in real terms across the Indian sub-continent. In Karnataka State dowries have been increasing substantially for several decades (Caldwell, Reddy and Caldwell, 1991) and dowries in these data average six times the annual income of a family. There are several possible reasons why dowries in India may have increased, but it is beyond the scope of this paper to examine them<sup>8</sup> . My focus here is on wedding celebrations, and in particular on the narrow question of whether wedding celebrations are, at least partly, driven by a desire for higher social rank.

Every wedding requires a minimum level of expenditure that depends upon norms in the community. A basic celebration may help maintain one's stature within the village, but a lavish wedding represents something else. If you spend more than what is expected this provides the

**8** For more on this subject, see Caldwell et. al (1988), Rao(1993), Raheja (1995) and Kapadia (1999).

7
by comparisons across multiple contexts.

# **4 Research Design**

## **4.1 Data**

### **4.1.1 Refugee Populations**

We use refugee population data provided by UNHCR’s Global Data Service on refugee populations at a sub-national level. These data cover 2001 to 2019 for low- and middle-income countries.<sup>12</sup> We code these locations, which are a mix of regions, cities, camps, and geo-locations, to match the sub-national regions in GWP. We include populations who are displaced outside of their country of birth, which captures people who UNHCR classifies as refugees, asylum-seekers, Venezuelans displaced abroad, and others of concern (all of whom are referred to as “refugees” throughout this paper).<sup>13</sup> While populations are estimated at the country level for every year and displaced nationality, sub-national data is missing for 33% of the total refugee population in LMICs between 2005 and 2018.<sup>14</sup> We therefore develop a methodology to impute the missing population totals at the region-origin-year level. We use the regional proportions of the population for country-origin-years where at least 70% of the refugee population’s location is known and combine this measure with the country-origin-year totals to impute the region-origin’s total population over time.<sup>15</sup> This methodology yields a balanced panel of refugee populations by nationality at the sub-national level. For the analysis presented here, refugee populations are aggregated at the region-year level, combining refugees from all countries of origin.

> 12The population totals are as of December of the reporting year.

> 13This excludes internally displaced people, returnees, and stateleses populations who are also under UNHCR’s mandate. It also excludes Palestinians who are under the mandate of United Nations Relief and Works Agency.

> 14Sub-national data is also missing for almost all of the high-income countries, which also drives our choice to focus on LMICs here.

> 15Further details are available upon request.

9
Table 1: Selected summary statistics

||Non-migrant|Households|||Migrant House|holds|Mean equality|
|---|---|---|---|---|---|---|---|
||N|Mean|(SD)|N|Mean|(SD)|(t-test)|
|Child characteristics||||||||
|Age|1783|12.28|(3.73)|718|12.68|(3.79)|ee|
|Male|1783|0.51||718|0.51|||
|Grade Point Average (GPA) (0-10)|1355|8.04|(LO7)|555|8.10|(0.93)|*|
|Serious illness (past year)|1783|0.29||TIS|0.26|||
|Distance to school (min)|1659|20.76|(18.39)|668|19.92|(17.53)||
|Household characteristics||||||||
|Total income|1783|S3819.11|(86592.44)|718|4890140|(49005.71)|ease|
|Household size|1783|4.70|(1.89)|718|4.13|(1.75)|eer|
|Mean years education|1782|10.74|(2.40)|718|10.68|(1.93)||
|Urban|1783|0.24||718|0.15||eer|
|Older siblings|1783|0.59||718|0.58|||
|Parents divorced|1783|0.12||718|0.10|||
|Private inputs to child's education||||||||
|Caregiver lime|1565|3.78|(1.94)|640|3.62|(1.97)|*|
|Payments to teachers|1552|89.09|(275.56)|635|O5.02|(163.85)|wr|
|Qut-of-school tutoring|1572|192.70|(1179.89)|642|86.57|(376.58)|eee|
|Transportation expenditures|1565|BOSS|(775.61)|644|20929|(902.19)||
|Nerwork-GrowthInstrument|Numberofcommunities|Mean|(SD)|Min|Max|||
||129|277.66|(140.22)|2.33|691.70|||